DETAILED ACTION
This action is responsive to the amendment filed for application 18/091,244 filed on 04/22/2026. Claims 1-2, 13-14, and 20 have been amended. Claims 1, 13 and 20 are independent claims. Claims 1-20 are pending in the case.
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 .
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.
Claim Rejections - 35 USC § 112
Claim 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, 13 and 20 recites the limitation training the artificial neural network based on the loss by penalizing the differences. There is insufficient antecedent basis for this limitation in the claim. The lack of antecedent basis renders the training based on the differences ambiguous as there are differences between the label space similarities and differences between rankings of the label space similarities already in the claim or if it is referring to a differences not yet recited.
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 1-9, 12-17 and 20 are rejected under 35 U.S.C. 101 because they claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining label space similarities between different ones of the plurality of training target targets as represented in the label which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining feature space similarities between different ones of the plurality of inputs as represented in the feature space which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate how correct a prediction is compared to the actual result. See 2106.04.(a)(2).III.C
Subject Matter Eligibility Analysis Step 2A Prong 2:
obtaining a training regression dataset comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs, and wherein the plurality of inputs are represented in a feature space and the plurality of training targets are represented in a label space of continuous value(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
training an artificial neural network using the training regression dataset (recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
training an artificial neural network based on the loss by penalizing the differences (recites insignificant extra-solution and well understood, routine, and conventional activity of training based on a loss(see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional element (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) recites a well understood and conventional practice of a neural network based on the loss as quoted from Minimizing the Maximal Loss: How and Why (Abstract, “A commonly used learning rule is to approximately minimize the average loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the maximal loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms”)
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites obtained by applying a first similarity function in the label space across the plurality of training targets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user solving a mathematical formula. See 2106.04.(a)(2).III.C and Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C)).
The claim recites obtained by applying a second similarity function in the feature space across the plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user solving a mathematical formula. See 2106.04.(a)(2).III.C and Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C)).
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the label space similarities are represented as a first pairwise similarity matrix(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the feature space similarities are represented as a second pairwise similarity matrix(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 3:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the first and second similarity functions differ which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 4:
The rejection of claim 3 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites first similarity function comprises negative absolute distance which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites the second similarity function comprises a cosine similarity which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 5:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the loss based on the differences between the label space similarities and feature space similarities is determined as L, wherein L comprises
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wherein Sy denotes the first pairwise similarity matrix, Sz denotes the second pairwise similarity matrix, [i,:] denotes an ith row of the matrices, rk denotes a ranking function, and ℓ penalizes differences between the pairwise similarity matrices which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 6:
The rejection of claim 5 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determines mean squared error between
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and
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which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 7:
The rejection of claim 5 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein training the artificial neural network comprises determining
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which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
The claim recites wherein
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wherein λ denotes interpolation strength and a denotes
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or
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which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 8:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the artificial neural network is trained based on minimizing the loss(recites insignificant extra-solution and well understood, routine, and conventional activity of training based on a loss(see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) recites a well understood and conventional practice of a neural network based on the loss as quoted from Minimizing the Maximal Loss: How and Why (Abstract, “A commonly used learning rule is to approximately minimize the average loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the maximal loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms”)
The additional element(s) (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 9:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the regression dataset is imbalanced(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 12:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites …determine a label corresponding to the label space based on the data point which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user making a judgement/determination. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
obtaining a data point of a type corresponding to the feature space(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
applying the artificial neural network to…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining label space similarities between different ones of the plurality of training target targets as represented in the label which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining feature space similarities between different ones of the plurality of inputs as represented in the feature space which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate how correct a prediction is compared to the actual result. See 2106.04.(a)(2).III.C
Subject Matter Eligibility Analysis Step 2A Prong 2:
obtaining a training regression dataset comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs, and wherein the plurality of inputs are represented in a feature space and the plurality of training targets are represented in a label space of continuous value(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
training an artificial neural network using the training regression dataset (recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
training an artificial neural network based on the loss by penalizing the differences (recites insignificant extra-solution and well understood, routine, and conventional activity of training based on a loss(see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional element (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) recites a well understood and conventional practice of a neural network based on the loss as quoted from Minimizing the Maximal Loss: How and Why (Abstract, “A commonly used learning rule is to approximately minimize the average loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the maximal loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms”)
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 14:
The rejection of claim 13 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 2.
Regarding Claim 15:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the first and second similarity functions differ which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites first similarity function comprises negative absolute distance which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites the second similarity function comprises a cosine similarity which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 16:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the loss based on the differences between the label space similarities and feature space similarities is determined as L, wherein L comprises
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wherein Sy denotes the first pairwise similarity matrix, Sz denotes the second pairwise similarity matrix, [i,:] denotes an ith row of the matrices, rk denotes a ranking function, and ℓ penalizes differences between the pairwise similarity matrices which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
The claim recites determines mean squared error between
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and
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which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites wherein training the artificial neural network comprises determining
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which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
The claim recites wherein
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wherein λ denotes interpolation strength and a denotes
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or
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which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 17:
The rejection of claim 13 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 9.
Regarding Claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining label space similarities between different ones of the plurality of training target targets as represented in the label which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining feature space similarities between different ones of the plurality of inputs as represented in the feature space which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation and judgement to determine similarities of a dataset. See 2106.04.(a)(2).III.C.
The claim recites determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate how correct a prediction is compared to the actual result. See 2106.04.(a)(2).III.C
Subject Matter Eligibility Analysis Step 2A Prong 2:
obtaining a training regression dataset comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs, and wherein the plurality of inputs are represented in a feature space and the plurality of training targets are represented in a label space of continuous value(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
training an artificial neural network using the training regression dataset (recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
training an artificial neural network based on the loss by penalizing the differences (recites insignificant extra-solution and well understood, routine, and conventional activity of training based on a loss(see MPEP 2106.05(g))
a processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
database storing a regression dataset comprising multiple pairs that is communicatively coupled to the processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
a memory that is communicatively coupled to the processor and that has stored thereon computer program code that is executable by the processor and that, when executed by the processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional element (b), (d), (e) and (f) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) recites a well understood and conventional practice of a neural network based on the loss as quoted from Minimizing the Maximal Loss: How and Why (Abstract, “A commonly used learning rule is to approximately minimize the average loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the maximal loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms”)
The additional element(s) (a) (b) (c) (d) (e) and (f) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
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.
Claim(s) 1-3, 8-14 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al.(“f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition”, henceforth known as Zhang).
Regarding Claim 1:
Zhang discloses obtaining a training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We experiment on IEMOCAP…and MSPImprov…We select these datasets” where the use of training datasets corresponds to obtaining a training regression dataset) comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs(Zhang, Page 5, Col. 1, Paragraph , “We focus on predicting binary valence and activation, where the classifiers are trained using soft labels”), and wherein the plurality of inputs are represented in a feature space(Zhang, Page 5, Col. 1, Paragraph 6, “We perform z-normalization for each feature dimension at the frame-level over each dataset, individually”) and the plurality of training targets are represented in a label space of continuous values(Zhang, Page 7, Col. 1, Paragraph 1, “These loss functions are designed for deep metric learning with soft labels, i.e., labels with continuous values along one or multiple dimensions”)
Zhang discloses training an artificial neural network using the training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We select these datasets because: (1) they are relatively large, which allows us to train neural networks”)
Zhang discloses determining label space similarities between different ones of the plurality of training targets as represented in the label space(Zhang, Page 2, Col. 2, Paragraph 5, “The function C : AY × AY -> [0, 2] measures label similarity”) and determining feature space similarities between different ones of the plurality of inputs as represented in the feature space(Zhang, Page 2, Col. 2, Paragraph 5, “A feature learning function (i.e., a neural network) g ϵ G, maps inputs from AX to a new space AG and S : AG × AG -> [0, 2] measures the similarity on AG” where S measuring the similarity between the inputs from AX that are mapped to a space AG corresponds to determining feature space similarities between different ones of the plurality of inputs as represented in the feature as it uses a feature learning function with the mapped input to measure similarity of inputs )
Zhang discloses determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs(Zhang, Page 4, Col. 2, Paragraph 4, “L(tri, g; ω) = Lcls(ya; y^a) + α(f-SPL(Ss(g);Cs) + f-SPL(Sd(g);Cd))” corresponds to determining a loss based on difference between label space and feature space similarities, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs as Cs/Cd and Ss(g)/Sd(g) corresponds to label and feature space similarities and the loss is penalized based on the ranking disagreement between S and C where the ranking is how similar/dissimilar score of S or C(See also Zhang, Page 2, Col. 2, Paragraph 5, “The optimal solution of F, g*, satisfies S(g*(xi); g*(xj)) = C(yi; yj) for every i ≠ j, i.e., the similarity between the examples on the learned space is the same as the similarity between their labels”)
Zhang discloses training an artificial neural network based on the loss by penalizing the differences(Zhang, Page 2, Col. 2, Paragraph 4, “Our goal is to learn an embedding space on which the similarity between examples equals to the label similarity. In Section 3.1, we define a family of loss functions, f-SPL, based on the dual form of f-divergence. Then in Section 3.2, we mathematically prove that we can achieve our goal by minimizing f-SPL” where minimizing the loss function by learning an embedding space where similarity between examples equals the label similarity corresponds to training an artificial neural network based on the loss by penalizing the differences(See also Zhang, Page 4, Col. 1, Theorem 3, where the Theorem states the minimizer that drives the loss to zero is S(g*(xi); g*(xj)) = C(yi; yj) which shows that that the error will be greater, which penalizes the model, as the difference between S and C grows))
Regarding Claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the label space similarities are represented as a first pairwise similarity matrix obtained by applying a first similarity function in the label space across the targets and wherein the feature space similarities are represented as a second pairwise similarity matrix obtained by applying a second similarity function in the feature space across the inputs(Zhang, Page 2, Col. 2, Paragraph 5, “The optimal solution of F, g*, satisfies S(g*(xi); g*(xj)) = C(yi; yj) for every i ≠ j, i.e., the similarity between the examples on the learned space is the same as the similarity between their labels” where C and S naturally form the two pairwise similarity matrices as inputs are mapped with a matrix of C[i, j] = C(yi,yj,) representing a label similarity matrix and the matrix of S[i,j] = S(g(xi), g(xj,)) representing a feature similarity matrix)
Regarding Claim 3:
The rejection of claim 2 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the first and second similarity functions differ(Zhang, Page 2, Col. 2, Paragraph 5, “The function C : AY × AY -> [0, 2] measures label similarity” and Zhang, Page 2, Col. 2, Paragraph 5, “A feature learning function (i.e., a neural network) g ϵ G, maps inputs from AX to a new space AG and S : AG × AG -> [0, 2] measures the similarity on AG” where S and C are different similarity functions)
Regarding Claim 8:
The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the artificial neural network is trained based on minimizing the loss(Zhang, Page 5, Col. 2, Paragraph 5, “Our goal is to learn an embedding space on which the similarity between examples equals to the label similarity. In Section 3.1, we define a family of loss functions, f-SPL, based on the dual form of f-divergence. Then in Section 3.2, we mathematically prove that we can achieve our goal by minimizing f-SPL.”)
Regarding Claim 9:
The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the regression dataset is imbalanced(Zhang, Page 6, Col. 1, Paragraph 3, “We use Unweighted Average Recall (UAR) as the performance measure due to data imbalance”)
Regarding Claim 10:
The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the artificial neural network is trained based on a total loss determined from the loss based on the differences between the label space similarities and feature space similarities(Zhang, Page 4, Col. 2, Paragraph 4, “The overall loss function for each triplet, tri, is
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29
684
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where the batch total loss is the average of the triplet losses and the network is trained to reduce the total loss with f-SPL penalizing the disagreement between label and feature space similarities)
Zhang discloses and also from one or more additional losses respectively determined by applying one or more imbalanced learning techniques(Zhang, Page 5, Col. 2, Paragraph 3, “We weigh the two classes using N/(
2
∑
i
=
1
N
y
i
c
)
in the loss calculation to reduce the influence of data imbalance. Here, N is the total number of training utterances,
y
i
c
is the value for class c in the label vector of data point i” where the technique used to reduce influence of data imbalance corresponds to an imbalanced learning technique and the use of the technique in the loss calculation corresponds to additional losses respectively determined by applying one or more imbalanced learning techniques)
Regarding Claim 11:
The rejection of claim 10 is incorporated and further claim recites further additional elements/limitations:
Zhang further discloses wherein the one or more imbalanced learning techniques comprise any one or more of re-weighting, two-stage training, and distribution smoothing(Zhang, Page 5, Col. 2, Paragraph 3, “We weigh the two classes using N/(
2
∑
i
=
1
N
y
i
c
)
in the loss calculation to reduce the influence of data imbalance. Here, N is the total number of training utterances,
y
i
c
is the value for class c in the label vector of data point i” where the weighing of classes using an imbalanced learning technique corresponds to imbalanced learning techniques comprising re-weighting)
Regarding Claim 12:
The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses after the training(Zhang, Page 4, Col. 2, Paragraph 6, “In the testing phase, the trained network takes batches of individual examples as the input.”): (a) obtaining a data point of a type corresponding to the feature space(Zhang, Page 5, Col. 1, Paragraph 6, “We preprocess the data such that the audio sampling rate is 16,000 Hz for both datasets. We then extract 40-dimensional log Mel-frequency Filterbank energy (MFB)”); and (b) applying the artificial neural network to determine a label corresponding to the label space based on the data point(Zhang, Page 4, Col. 2, Paragraph 3, “where ya = ω(g(xa)) is the prediction over classes” where ya is a neural network output y is a prediction of known soft label y assigned to input x (See also Zhang, Page 6, Col. 1, Paragraph 3, “In the testing phase, we convert the output of the network to a class prediction”)
Regarding Claim 13:
Zhang discloses obtaining a training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We experiment on IEMOCAP…and MSPImprov…We select these datasets” where the use of training datasets corresponds to obtaining a training regression dataset) comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs(Zhang, Page 5, Col. 1, Paragraph , “We focus on predicting binary valence and activation, where the classifiers are trained using soft labels”), and wherein the plurality of inputs are represented in a feature space(Zhang, Page 5, Col. 1, Paragraph 6, “We perform z-normalization for each feature dimension at the frame-level over each dataset, individually”) and the plurality of training targets are represented in a label space of continuous values(Zhang, Page 7, Col. 1, Paragraph 1, “These loss functions are designed for deep metric learning with soft labels, i.e., labels with continuous values along one or multiple dimensions”)
Zhang discloses training an artificial neural network using the training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We select these datasets because: (1) they are relatively large, which allows us to train neural networks”)
Zhang discloses determining label space similarities between different ones of the plurality of training targets as represented in the label space(Zhang, Page 2, Col. 2, Paragraph 5, “The function C : AY × AY -> [0, 2] measures label similarity”) and determining feature space similarities between different ones of the plurality of inputs as represented in the feature space(Zhang, Page 2, Col. 2, Paragraph 5, “A feature learning function (i.e., a neural network) g ϵ G, maps inputs from AX to a new space AG and S : AG × AG -> [0, 2] measures the similarity on AG” where S measuring the similarity between the inputs from AX that are mapped to a space AG corresponds to determining feature space similarities between different ones of the plurality of inputs as represented in the feature as it uses a feature learning function with the mapped input to measure similarity of inputs )
Zhang discloses determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs(Zhang, Page 4, Col. 2, Paragraph 4, “L(tri, g; ω) = Lcls(ya; y^a) + α(f-SPL(Ss(g);Cs) + f-SPL(Sd(g);Cd))” corresponds to determining a loss based on difference between label space and feature space similarities, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs as Cs/Cd and Ss(g)/Sd(g) corresponds to label and feature space similarities and the loss is penalized based on the ranking disagreement between S and C where the ranking is how similar/dissimilar score of S or C(See also Zhang, Page 2, Col. 2, Paragraph 5, “The optimal solution of F, g*, satisfies S(g*(xi); g*(xj)) = C(yi; yj) for every i ≠ j, i.e., the similarity between the examples on the learned space is the same as the similarity between their labels”)
Zhang discloses training an artificial neural network based on the loss by penalizing the differences(Zhang, Page 2, Col. 2, Paragraph 4, “Our goal is to learn an embedding space on which the similarity between examples equals to the label similarity. In Section 3.1, we define a family of loss functions, f-SPL, based on the dual form of f-divergence. Then in Section 3.2, we mathematically prove that we can achieve our goal by minimizing f-SPL” where minimizing the loss function by learning an embedding space where similarity between examples equals the label similarity corresponds to training an artificial neural network based on the loss by penalizing the differences(See also Zhang, Page 4, Col. 1, Theorem 3, where the Theorem states the minimizer that drives the loss to zero is S(g*(xi); g*(xj)) = C(yi; yj) which shows that that the error will be greater, which penalizes the model, as the difference between S and C grows))
Regarding Claim 14:
The rejection of claim 13 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 2.
Regarding Claim 17:
The rejection of claim 13 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 9.
Regarding Claim 18:
The rejection of claim 13 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 10.
Regarding Claim 19:
The rejection of claim 18 is incorporated and, further, is rejected under the same rationale as set forth in the rejection of claim 11.
Regarding Claim 20:
Zhang discloses a processor(Zhang, Page 6, Col. 1, Paragraph 4, “We experiment using PyTorch version 0.2.0)…a database storing a training regression dataset (Zhang, Page 5, Col. 1, Paragraph 1, “We experiment on IEMOCAP…and MSPImprov…We select these datasets” where the use of training datasets corresponds to obtaining a training regression dataset) that is communicatively coupled to the processor and…a memory that is communicatively coupled to the processor and that has stored thereon computer program code that is executable by the processor and that, when executed by the processor(where the experiments conducted using PyTorch requires a processor and memory to be in communication with each other)
Zhang discloses obtaining a training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We experiment on IEMOCAP…and MSPImprov…We select these datasets” where the use of training datasets corresponds to obtaining a training regression dataset) comprising a plurality of inputs and a plurality of training targets, each of the plurality of training targets corresponding to a respective one of the plurality of inputs(Zhang, Page 5, Col. 1, Paragraph , “We focus on predicting binary valence and activation, where the classifiers are trained using soft labels”), and wherein the plurality of inputs are represented in a feature space(Zhang, Page 5, Col. 1, Paragraph 6, “We perform z-normalization for each feature dimension at the frame-level over each dataset, individually”) and the plurality of training targets are represented in a label space of continuous values(Zhang, Page 7, Col. 1, Paragraph 1, “These loss functions are designed for deep metric learning with soft labels, i.e., labels with continuous values along one or multiple dimensions”)
Zhang discloses training an artificial neural network using the training regression dataset(Zhang, Page 5, Col. 1, Paragraph 1, “We select these datasets because: (1) they are relatively large, which allows us to train neural networks”)
Zhang discloses determining label space similarities between different ones of the plurality of training targets as represented in the label space(Zhang, Page 2, Col. 2, Paragraph 5, “The function C : AY × AY -> [0, 2] measures label similarity”) and determining feature space similarities between different ones of the plurality of inputs as represented in the feature space(Zhang, Page 2, Col. 2, Paragraph 5, “A feature learning function (i.e., a neural network) g ϵ G, maps inputs from AX to a new space AG and S : AG × AG -> [0, 2] measures the similarity on AG” where S measuring the similarity between the inputs from AX that are mapped to a space AG corresponds to determining feature space similarities between different ones of the plurality of inputs as represented in the feature as it uses a feature learning function with the mapped input to measure similarity of inputs )
Zhang discloses determining a loss based on differences between the label space similarities and feature space similarities that correspond to each other, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs(Zhang, Page 4, Col. 2, Paragraph 4, “L(tri, g; ω) = Lcls(ya; y^a) + α(f-SPL(Ss(g);Cs) + f-SPL(Sd(g);Cd))” corresponds to determining a loss based on difference between label space and feature space similarities, wherein the loss is based on differences between rankings of the label space similarities for each of the plurality of training targets and rankings of the feature space similarities for a corresponding one of the plurality of inputs as Cs/Cd and Ss(g)/Sd(g) corresponds to label and feature space similarities and the loss is penalized based on the ranking disagreement between S and C where the ranking is how similar/dissimilar score of S or C(See also Zhang, Page 2, Col. 2, Paragraph 5, “The optimal solution of F, g*, satisfies S(g*(xi); g*(xj)) = C(yi; yj) for every i ≠ j, i.e., the similarity between the examples on the learned space is the same as the similarity between their labels”)
Zhang discloses training an artificial neural network based on the loss by penalizing the differences(Zhang, Page 2, Col. 2, Paragraph 4, “Our goal is to learn an embedding space on which the similarity between examples equals to the label similarity. In Section 3.1, we define a family of loss functions, f-SPL, based on the dual form of f-divergence. Then in Section 3.2, we mathematically prove that we can achieve our goal by minimizing f-SPL” where minimizing the loss function by learning an embedding space where similarity between examples equals the label similarity corresponds to training an artificial neural network based on the loss by penalizing the differences(See also Zhang, Page 4, Col. 1, Theorem 3, where the Theorem states the minimizer that drives the loss to zero is S(g*(xi); g*(xj)) = C(yi; yj) which shows that that the error will be greater, which penalizes the model, as the difference between S and C grows))
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al.(“f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition” henceforth known as Zhang) in view of Dong et al(“Individual Fairness for Graph Neural Networks: A Ranking based Approach” henceforth known as Dong)
Regarding Claim 4:
The rejection of claim 3 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the first similarity function comprises negative absolute distance(Zhang, Page 5, Col. 2, Paragraph 5, “The label similarity, C ∈ [0,2], is calculated by 2−2d, where d is the total variation distance (∈ [0,1]) between a pair of labels”)
Zhang does not disclose, however Dong discloses and the second similarity function comprises a cosine similarity(Dong, Page 6, Col. 1, Paragraph 1, “we compute the cosine similarity between input node features as the SG”)
References Zhang and Dong are analogous art because they are from the [insert the phrase “same field of endeavor” or “problem-solving area,” and the name of that field or area.]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhang and Dong before him or her, to modify the second similarity function of Zhang to include the cosine similarity function of Dong because as a reasonable substitute for handling representations in embedding space. The suggestion/motivation for doing so would have been Zhang, Page 6, Col. 1, Paragraph 1, “we utilize the cosine distance, which is the most widely adopted distance metric to measure node pair similarity in the embedding space.”
Regarding Claim 15:
The rejection of claim 14 is incorporated and further claim recites further additional elements/limitations:
Zhang discloses wherein the first and second similarity functions differ(Zhang, Page 2, Col. 2, Paragraph 5, “The function C : AY × AY -> [0, 2] measures label similarity” and Zhang, Page 2, Col. 2, Paragraph 5, “A feature learning function (i.e., a neural network) g ϵ G, maps inputs from AX to a new space AG and S : AG × AG -> [0, 2] measures the similarity on AG” where S and C are different similarity functions)
Zhang discloses wherein the first similarity function comprises negative absolute distance(Zhang, Page 5, Col. 2, Paragraph 5, “The label similarity, C ∈ [0,2], is calculated by 2−2d, where d is the total variation distance (∈ [0,1]) between a pair of labels”)
Zhang does not disclose, however Dong discloses the second similarity function comprises a cosine similarity(Dong, Page 6, Col. 1, Paragraph 1, “we compute the cosine similarity between input node features as the SG”)
References Zhang and Dong are analogous art because they are from the [insert the phrase “same field of endeavor” or “problem-solving area,” and the name of that field or area.]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhang and Dong before him or her, to modify the second similarity function of Zhang to include the cosine similarity function of Dong because as a reasonable substitute for handling representations in embedding space. The suggestion/motivation for doing so would have been Zhang, Page 6, Col. 1, Paragraph 1, “we utilize the cosine distance, which is the most widely adopted distance metric to measure node pair similarity in the embedding space.”
Response to Arguments:
Applicant's arguments filed 04/22/2026 have been fully considered but they are not persuasive. A breakdown of arguments can be found below.
102/103:
Applicant’s arguments with respect to claim(s) 1-20 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.
101:
Applicant appears to argue on page 9 that a human mind cannot determine a lose based on differences between rankings of the label space similarities for each training target and rankings of the feature space similarities for a corresponding input. Applicant continues and appears to argue that a the human mind cannot practically train the artificial neural network based on such a loss.
Examiner respectfully disagrees as Applicant appears to be interpreting a narrower claim as the current claims do not positively recite providing a specific method for determining a loss that a human mind is not able to performed. Examiner disagrees that a human mind cannot perform the limitation as he broadest reasonable interpretation of determining a loss based on observed differences/similarities of rankings is calculating or choosing a penalty or objective value representing how much the ordering of similarities disagree. Additionally, Examiner categorized the training based on a loss as an additional element and not a mental/abstract idea, which is separate from the determination of the loss.
Applicant appears to argue on page 10-13 that determination of label and feature space similarities for use in formulating a loss based on differences to train an artificial neural network by penalizing the differences in similarity rankings addresses a technical problem of data balance in model training for cases where the training data includes unevenly distributed training targets thereby providing a technological improvement to a technical problem. Applicant further argues the application should be eligible as whole while citing to 2106.04(d)(III), the August 4th memo and Desjardins for support for eligibility.
Examiner respectfully disagrees as Applicant appears to be interpreting a narrower claim as the current claims do not positively recite providing a specific method for handling imbalanced data and the argued improvement focused on is the determination step which is classified as an abstract idea and training based on a loss by penalizing the differences is insignificant extra-solution and well understood, routine, and conventional activity (see MPEP 2106.05(g). Examiner’s review of the arguments and application understands the improvement being provided by the claimed abstract idea determining of a label and feature space similarities for use in formulating a loss, which does not result in an improvement in technology, but merely is an improvement in the abstract idea of determining similarities. Substantial additional elements should reflect the improvement of handling imbalanced data and applicant has highlighted an abstract idea and a well understood, routine, and conventional activity that Applicant asserts reflects the improvement.
MPEP 2106.04(d)(III) and the August 4th memo referenced by Applicant discusses additional elements interaction with judicial exceptions may integrate into a practical application and not to additional elements by themselves. Specifically, on whether the additional elements and limitations impact each other and to view the claims as a whole. Additionally, Ex Parte Desjardins defines the specific continual-learning parameter preservation mechanism of training on a second task using posterior distribution from the first task in the claim as reflecting the improvement. As discussed above, such elements/limitations in the current claims do not reflect such an improvement. At present, the current claims and arguments only states an improvement is achieved however no specific steps or limitations of the training, neural network architecture, data handling or loss calculations reflects the optimization/improvement.
Conclusion
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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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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