DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed June 3rd, 2026, in which claims 1-8 and 10-14 have been amended. No claims have been cancelled nor added. The amendments have been entered, and claims 1-14 are currently pending in the case. Claims 1, 4, 8 and 11 are independent claims.
Specification
A substitute specification in proper idiomatic English and in compliance with 37 CFR 1.52(a) and (b) is required. The substitute specification filed must be accompanied by a statement that it contains no new matter.
A non-exhaustive list of examples includes:
“As one aspect, it is possible to mapping for accurately separate normal data and anomalous data, and to accurately detecting anomalies based on the result of mapping.” in ¶13.
“That is, the inventor derived a model that accurately separates and maps the feature vectors of normal data and the feature vectors of anomalous data in monitoring of a control system, and serves as a meaningful product that could not possibly be humanly created.” in ¶31.
“Also, the determination result may not only be the two values normal and anomalous, and a plurality of levels may be provided for anomalous.” in ¶85/106.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Claim 1 is directed to [a] learning apparatus, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“learn a first parameter and a second parameter that are included…” this encompasses the mental learning of observed parameters.
“map a feature vector generated based on normal data…to a region, which has been set based on (i) a subspace preset in advance… and (ii) a distance function independently defined with respect to the subspace and adjustable…, wherein the first parameter is for generating the feature vector and the second parameter is for adjusting the distance function with respect to the subspace” this encompasses the mental mapping to an observed subspace based on a mentally created feature vector. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “one or more memories storing instructions”, “a mapping model” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “one or more processors configured to execute the instructions to”, “the mapping model being configured to”, “by the mapping model”, “by the mapping model” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “input as training data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “input as training data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claim 2, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“select, as the subspace, at least one non-spherical subspace including a hyperplane, a hyperellipsoid with unequal principal axes, a hyperboloid, or a torus” this encompasses the mental selection of a subspace.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “one or more processors is further configured to execute the instructions to” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 3, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“reconstructing input data corresponding to the feature vector” this encompasses the mental reconstruction of data corresponding to an observed feature vector.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the one or more processors is further configured to execute the instructions to” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “receive input of the feature vector of the normal data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receive input of a feature vector of the normal data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claim 4:
Step 1: Claim 4 is directed to [a]n anomaly detection apparatus, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“map a feature vector generated based on normal data input as training data to a region, which has been set based on (i) a subspace preset in advance…and (ii) a distance function independently defined with respect to the subspace and adjustable…” this encompasses the mental mapping of an observed feature vector to a subspace.
“determine that the feature vector is anomalous based on a result of the mapping” this encompasses the mental determination that an observed feature vector is anomalous based on a result of observed mapping.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “one or more memories storing instructions”, “a mapping model” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “one or more processors configured to execute the instructions to”, “the mapping model being configured to”, “by the mapping model”, “by the mapping model” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “provide input data acquired from a target system” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “provide input data acquired from a target system” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claim 5, the rejection of claim 4 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“determine that the feature vector mapped outside the region is anomalous” this encompasses the mental determination of that an observed feature vector is anomalous.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “wherein the one or more processors is further configured to execute the instructions to” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 6, the rejection of claim 4 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“reconstructing input data corresponding to the feature vector” this encompasses the mental reconstruction of data corresponding to an observed feature vector.
“calculate a reconstruction error representing a difference between the input data and reconstructed data” this limitation is a mathematical concept.
“determine an anomaly of the feature vector, based on a result of the mapping and the reconstruction error” this encompasses the mental determination of an anomaly of an observed feature vector, based on a result of the mapping and an observed reconstruction error.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the one or more processors is further configured to execute the instructions to”, “obtained by inputting the feature vector of the input data to the autoencoder” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “receive input of a feature vector of normal data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 7, the rejection of claim 4 is incorporated and further:
Step 2A Prong 1: a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the input data includes one of traffic data of a network in the target system and sensor data output from a sensor” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 8:
Step 1: Claim 8 is directed to [a] learning method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“learning a first parameter and a second parameter that are included…” this encompasses the mental learning of observed parameters.
“map a feature vector generated based on normal data…to a region, which has been set based on (i) a subspace preset in advance and (ii) a distance function independently defined with respect to the subspace and adjustable…, wherein the first parameter is for generating the feature vector and the second parameter is for adjusting the distance function with respect to the subspace” this encompasses the mental learning of observed parameters for mapping to an observed subspace based on a mentally created feature vector. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “a mapping model” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “the mapping model being configured to”, “by the mapping model” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “input as training data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “input as training data” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claims 9-10:
The rejection of claim 8 is further incorporated, the rejection of claims 2-3 are applicable to claims 9-10, respectively.
Regarding claim 11:
Step 1: Claim 11 is directed to [a]n anomaly detection method, therefore it falls under the statuary category of a process.
“mapping a feature vector generated based on normal data input as training data to a region, which has been set based on (i) a subspace preset in advance…and (ii) a distance function independently defined with respect to the subspace and adjustable…” this encompasses the mental mapping of an observed feature vector to a subspace.
“determine that the feature vector is anomalous based on a result of the mapping” this encompasses the mental determination that an observed feature vector is anomalous based on a result of observed mapping.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “a mapping model” “the mapping model being configured to”, “by the mapping model”, “by the mapping model” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “providing input data acquired from a target system” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “providing input data acquired from a target system” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claims 12-14:
The rejection of claim 11 is further incorporated, the rejection of claims 5-7 are applicable to claims 12-14, respectively.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 8-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ruff et al. (“Deep One-Class Classification”, Ruff et al., 2018) (as cited in the IDS, hereinafter “Ruff”).
Regarding claim 1:
1. (Currently Amended) A learning apparatus comprising:
Ruff teaches [a] learning apparatus comprising:
one or more memories storing instructions (Ruff, page 6, footnote 2 “We provide our code at https://github.com/lukasruff/Deep SVDD.” Here, the code used for Deep SVDD discloses a memory storing instructions); and
one or more processors configured to execute the instructions (Ruff, page 4, col 2, section 3.2, ¶1 “Deep SVDD to scale well with large datasets as its computational complexity scales linearly in the number of training batches and each batch can be processed in parallel (e.g. by processing on multiple GPUs)” here, the use of GPUs discloses processors executing the instructions) to:
learn a first parameter and a second parameter that are included in a mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.”), the mapping model being configured to map a feature vector generated based on normal data input as training data to a region (Ruff, page 4, col 1, ¶2 “Given some training data Dn = {x1,...,xn} on X, we define the soft-boundary Deep SVDD objective as
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As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, W can be considered the first parameter, R can be considered the second parameter, the hypersphere can be considered the subspace, with R (the radius) being the distance from the subspace and Dn can be considered the feature vector of normal data based on the training data), which has been set based on (i) a subspace preset in advance by the mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.” Here, the hypersphere can be considered the subspace set in advance) and (ii) a distance function independently defined with respect to the subspace and adjustable by the mapping model (Ruff, page 4, col 1, ¶2 “The second term is a penalty term for points lying outside the sphere after being passed through the network, i.e. if its distance to the center φ(xi;W) − c is greater than radius R.” here, φ(xi;W) – c can be considered the distance function independently defined with respect to the subspace in light of the specification, see equation 1),
wherein the first parameter is for generating the feature vector (Ruff, page 4, col 1, ¶2 “and set of weights W = {W1,...,W L} where W l are the weights of layer l ∈ {1,...,L}. That is, φ(x;W) ∈ F is the feature representation of x ∈ X given by network φ with parameters W.” here, the first parameter W, is used to generate the feature vector) and the second parameter is for adjusting a value of the distance function with respect to the subspace (Ruff, page 4, col 1, ¶2 “As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, the second parameter, radius R can be considered the distance).
Regarding claim 3:
Ruff teaches [t]he learning apparatus according to claim 1,
wherein the one or more processors is further configured to execute the instructions to receive input of the feature vector of the normal data and reconstructing input data corresponding to the feature vector (Ruff, page 2, figure 1 “Deep SVDD learns a neural network transformation φ(·;W) with weights W from input space X ⊆ Rd to output space F ⊆Rp that attempts to map most of the data network representations into a hypersphere characterized by center c and radius R of minimum volume.”).
Regarding claim 4:
Ruff teaches [a]n anomaly detection apparatus (Ruff, page 1, abstract “In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective.”) comprising:
one or more memories storing instructions (Ruff, page 6, footnote 2 “We provide our code at https://github.com/lukasruff/Deep SVDD.” Here, the code used for Deep SVDD discloses a memory storing instructions); and
one or more processors configured to execute the instructions (Ruff, page 4, col 2, section 3.2, ¶1 “Deep SVDD to scale well with large datasets as its computational complexity scales linearly in the number of training batches and each batch can be processed in parallel (e.g. by processing on multiple GPUs)” here, the use of GPUs discloses processors executing the instructions) to:
provide input data acquired from a target system to a mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.”), the mapping model being configured to map a feature vector generated based on normal data input as training data to a region (Ruff, page 4, col 1, ¶2 “Given some training data Dn = {x1,...,xn} on X, we define the soft-boundary Deep SVDD objective as
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As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, W can be considered the first parameter, R can be considered the second parameter, the hypersphere can be considered the subspace, with R (the radius) being the distance from the subspace and Dn can be considered the feature vector of normal data based on the training data), which has been set based on (i) a subspace preset in advance by the mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.” Here, the hypersphere can be considered the subspace set in advance) and (ii) a distance function independently defined with respect to the subspace and adjustable by the mapping model (Ruff, page 4, col 1, ¶2 “The second term is a penalty term for points lying outside the sphere after being passed through the network, i.e. if its distance to the center φ(xi;W) − c is greater than radius R.” here, φ(xi;W) – c can be considered the distance function independently defined with respect to the subspace in light of the specification, see equation 1); and
determine that a feature vector is anomalous based on a result of the mapping (Ruff, page 4, col 1, ¶4 “As a result, normal examples of the data are closely mapped to center c, whereas anomalous examples are mapped further away from the center or outside of the hypersphere.”).
Regarding claim 5:
Ruff teaches [t]he anomaly detection apparatus according to claim 4,
wherein the one or more processors is further configured to execute the instructions to determine that the feature vector mapped outside the region is anomalous (Ruff, page 4, col 1, ¶4 “As a result, normal examples of the data are closely mapped to center c, whereas anomalous examples are mapped further away from the center or outside of the hypersphere.”).
Regarding claim 8:
Ruff teaches [a] learning method comprising:
learning a first parameter and a second parameter that are included in a mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.”), the mapping model being configured to map a feature vector generated based on normal data input as training data to a region (Ruff, page 4, col 1, ¶2 “Given some training data Dn = {x1,...,xn} on X, we define the soft-boundary Deep SVDD objective as
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As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, W can be considered the first parameter, R can be considered the second parameter, the hypersphere can be considered the subspace, with R (the radius) being the distance from the subspace and Dn can be considered the feature vector of normal data based on the training data), which has been set based on (i) a subspace preset in advance by the mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.” Here, the hypersphere can be considered the subspace set in advance) and (ii) a distance function independently defined with respect to the subspace and adjustable by the mapping model (Ruff, page 4, col 1, ¶2 “The second term is a penalty term for points lying outside the sphere after being passed through the network, i.e. if its distance to the center φ(xi;W) − c is greater than radius R.” here, φ(xi;W) – c can be considered the distance function independently defined with respect to the subspace in light of the specification, see equation 1),
wherein the first parameter is for generating the feature vector (Ruff, page 4, col 1, ¶2 “and set of weights W = {W1,...,W L} where W l are the weights of layer l ∈ {1,...,L}. That is, φ(x;W) ∈ F is the feature representation of x ∈ X given by network φ with parameters W.” here, the first parameter W, is used to generate the feature vector) and the second parameter is for adjusting a value of the distance function with respect to the subspace (Ruff, page 4, col 1, ¶2 “As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, the second parameter, radius R can be considered the distance).
Regarding claim 10:
The rejection of claim 3 is applicable to claim 10.
Regarding claim 11:
Ruff teaches [a]n anomaly detection method comprising:
providing input data acquired from a target system to a mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.”), the mapping model being configured to map a feature vector generated based on normal data input as training data to a region (Ruff, page 4, col 1, ¶2 “Given some training data Dn = {x1,...,xn} on X, we define the soft-boundary Deep SVDD objective as
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192
653
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As in kernel SVDD, minimizing R2 minimizes the volume of the hypersphere.” Here, W can be considered the first parameter, R can be considered the second parameter, the hypersphere can be considered the subspace, with R (the radius) being the distance from the subspace and Dn can be considered the feature vector of normal data based on the training data), which has been set based on (i) a subspace preset in advance by the mapping model (Ruff, page 4, col 1, ¶1 “To do this we employ a neural network that is jointly trained to map the data into a hypersphere of minimum volume.” Here, the hypersphere can be considered the subspace set in advance) and (ii) a distance function independently defined with respect to the subspace and adjustable by the mapping model (Ruff, page 4, col 1, ¶2 “The second term is a penalty term for points lying outside the sphere after being passed through the network, i.e. if its distance to the center φ(xi;W) − c is greater than radius R.” here, φ(xi;W) – c can be considered the distance function independently defined with respect to the subspace in light of the specification, see equation 1); and
determining that a feature vector is anomalous based on a result of the mapping (Ruff, page 4, col 1, ¶4 “As a result, normal examples of the data are closely mapped to center c, whereas anomalous examples are mapped further away from the center or outside of the hypersphere.”).
Regarding claim 12:
The rejection of claim 5 is applicable to claim 12.
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.
Claims 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ruff in view of Erfani et al. (“High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning”, Erfani et al., 2016) (hereinafter “Erfani”).
Regarding claim 2:
Ruff teaches [t]he learning apparatus according to claim 1, comprising:
wherein the one or more processors is further configured to execute the instructions to select, as the subspace, at least one non-spherical subspace including a hyperplane (Erfani, page 5, col 2, section 3.2.2, ¶1 “PSVM identifies anomalies in the feature space by findinga hyperplane that best separates the data from the origin.” Here, the PSVM discloses a hyperplane. It is noted the claim recites alternative language, and Ruff in view of Erfani teaches at least one of the alternatives.), a hyperellipsoid with unequal principal axes, a hyperboloid, or a torus.
Ruff and Erfani are analogous art because both references concern methods for deep one-class learning for anomaly detection. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ruff’s anomaly detection system to incorporate the hyperplane taught by Erfani. The motivation for doing so would have been to be scalable and computationally efficient as stated in Erfani, page 1, Abstract “Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient.”
Regarding claim 6:
Ruff teaches [t]he anomaly detection apparatus according to claim 4,
wherein the one or more processors is further configured to execute the instructions to receive input of the feature vector of normal data and reconstructing input data corresponding to the feature vector,
Ruff does not teach “in the determination, calculate a reconstruction error representing a difference between the input data and reconstructed data obtained by inputting the feature vector of the input data to an autoencoder, and determine an anomaly of the feature vector, based on a result of the mapping and the reconstruction error”
However, Erfani teaches in the determination, calculate a reconstruction error representing a difference between the input data and reconstructed data obtained by inputting the feature vector of the input data to an autoencoder, and determine an anomaly of the feature vector, based on a result of the mapping and the reconstruction error (Erfani, page 6, col 1, section 4.1, ¶1 “The whole process of pre training and fine-tuning was performed in an unsupervised manner so far. When the autoencoder is used for anomaly detection, anomalies can be identified based on the history of the squared error between the inputs and outputs for the training records. Let e be the set of reconstruction error values of the
x
i
∈
X
, where i = 1,…,m. If the reconstruction error for a test sample is larger than the threshold
τ
=
μ
ⅇ
+
3
σ
ⅇ
, where
μ
ⅇ
and
σ
ⅇ
are the mean and standard deviation of the values in the set e, respectively, then the record is identified as anomalous, otherwise it is identified as normal.”).
Ruff and Erfani are analogous art because both references concern methods for deep one-class learning for anomaly detection. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ruff’s anomaly detection system to incorporate the reconstruction error taught by Erfani. The motivation for doing so would have been to be scalable and computationally efficient as stated in Erfani, page 1, Abstract “Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient.”
Regarding claim 9:
The rejection of claim 2 is applicable to claim 9.
Regarding claim 7:
Ruff teaches [t]he anomaly detection apparatus according to claim 4
Ruff does not teach “ wherein the input data includes one of traffic data of a network in the target system and sensor data output from a sensor “
However, Erfani teaches wherein the input data includes one of traffic data of a network in the target system and sensor data output from a sensor (Erfani, page 6, col 2, ¶2 “The real-life datasets are from the UCI Machine Learning Repository: (i) Forest Adult Gas Sensor Array Drift (Gas),…” It is noted the claim recites alternative language, and Erfani teaches at least one of the alternatives.).
Ruff and Erfani are analogous art because both references concern methods for deep one-class learning for anomaly detection. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ruff’s anomaly detection system to incorporate the sensor data taught by Erfani. The motivation for doing so would have been to be scalable and computationally efficient as stated in Erfani, page 1, Abstract “Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient.”
Regarding claims 13-14:
The rejections of claims 6-7 are applicable to claims 13-14, respectively.
Response to Arguments
Applicant's arguments filed June 3rd, 2026 (hereinafter “Remarks”) have been fully considered but they are not persuasive.
Regarding the objections to the claims, Applicant’s amended claims have overcome the objections, which are withdrawn.
Applicant’s arguments regarding the 35 U.S.C. 112(b) rejections of the previous office action have been fully considered, and are persuasive. The rejections have been withdrawn due to claim amendments.
Applicant’s arguments with respect to the 35 U.S.C. § 102/3 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Rejections under 35 U.S.C. § 101:
Argument 1:
“Amended claims do not recite any judicial exception. For example, the subject matter of amended claim 1 is directed to an apparatus performing a specific combination of operations in a particular sequence. As stated in the USPTO Memorandum dated August 4, 2025 ("Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101"), claims that merely involve a judicial exception are not claims that recite a judicial exception. Thus, the subject matter of amended claim 1 does not recite a judicial exception.” (Remarks, page 5).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility. The BRI sets the boundaries of the coverage sought by the claim and will influence whether the claim seeks to cover subject matter that is beyond the four statutory categories or encompasses subject matter that falls within the exceptions.” See MPEP § 2106(II). Further, “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.” See MPEP § 2106.04(a)(2)(III). The broadest reasonable interpretation of the claims includes a series of mental processes, with or without the aid of a pen and paper, such as the mental learning of observed parameters and mental mapping observed vectors to various spaces. Therefore, the claims were found to recite a judicial exception in Step 2A Prong 1.
Argument 2:
“Amended claims integrate any alleged judicial exception into a practical application. The claimed subject matter, by utilizing a mapping model and a distance function adjustable by the mapping model, improves the computational efficiency at least by reducing the amount of data that needs to be mapped, as described in paragraphs [0005] and [0006] of the specification. As stated in the USPTO Memorandum dated December 5, 2025 ("Advance notice of change to the MPEP in light of Ex Parte Desjardins"), a claim as a whole, including a limitation reflecting the improvement disclosed in the specification, integrates what would otherwise be a judicial exception into a practical application. Amended claims explicitly tie the claimed invention into the particular practical application.” (Remarks, page 6).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.” See MPEP § 2106.5(f). Here, the claimed computational efficiency brought by at least by reducing the amount of data that needs to be mapped cannot be considered to integrate what would otherwise be a judicial exception into a practical application.
Argument 3:
“As explained above, amended claims recite a technical implementation for solving a specific technical problem. In addition, the subject matter of amended claims recite a specific combination of operations, where such combination is not taught by the cited references. Therefore, Applicant respectfully submits that the additional elements now recited in amended claims amount to significantly more than any alleged judicial exception.” (Remarks, page 6).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103 ) is further discussed in MPEP § 2106.05(d).” See MPEP § 2106.05(I).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ribeiro et al. ("One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding", Ribeiro et al., May 20, 2020) discloses using Equation 14,
PNG
media_image2.png
117
330
media_image2.png
Greyscale
, to compute the distance, in the hyperspace, of each data point (of the test set) to the OC-SVM’s decision border. We represent this distance as either positive or negative. A positive distance indicates that the given data point is within the hypersphere, whilst a negative distance indicates that it is outside the decision border.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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