DETAILED ACTION
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 responsive to the Application filed on 12/29/2023. Claims 1-20 are pending in the case. Claims 1, 11, and 20 are independent claims.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “wherein the machine learning model is a reactive model.” The term “reactive” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of prior art and subject matter eligibility analyses Examiner assumes any model is a reactive model. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “wherein the machine learning model … decreases an error.” It is unclear what reference point the decrease is measured from. For the purposes of prior art and subject matter eligibility analyses Examiner assumes any degree of error is a decrease from some other degree. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “wherein the machine learning model … predicting an average of metrics.” It is unclear what other metrics exist beyond the one determined by the claim or how they are averaged together. For the purposes of prior art and subject matter eligibility analyses Examiner assumes this is an intended use carrying no patentable weight. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “wherein each metric corresponds to a respective set of features.” It is unclear how this limits the claim because the one metric determined by the claim inherently corresponds to the “set of features” input to the claim. For the purposes of prior art and subject matter eligibility analyses Examiner assumes this is an intended use carrying no patentable weight. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-5, 8-14, and 17-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Jain et al. (US 2023/0170094 A1, hereinafter Jain) in view of Ozay et al. (US 2023/0145919 A1, hereinafter Ozay).
As to independent claim 1, Jain teaches a computer implemented method, comprising:
inputting (“A biosignal 701 is input and processed,” paragraph 0038 line 1), by at least one processor (“one or more processor cores,” paragraph 0006 lines 2-3), a set of features into a machine learning model, wherein the machine learning model comprises a neural network (“The DenseNet 712 shown in FIG. 7 may be implemented with a form of a deep neural network 712,” paragraph 0046 lines 1-2) and a auto-encoder (“The encoder 704 and decoder 706 form an autoencoder 702,” paragraph 0044 lines 1-2);
determining, by the at least one processor and using the neural network, a first vector based on the set of features (“The tensorform biosignal 710 is input into a Dense network 712 to produce a feature vector 714,” paragraph 0041 lines 1-2);
determining, by the at least one processor and using the auto-encoder, a second vector based on the set of features (“A biosignal 701 is input and processed through an encoder 704 which performs a lossy compression. The minimized representation of this lossy compression, called the latent space 708, serves as input to two layers: a concatenation layer 716 (used later on),” paragraph 0038 line 1 to paragraph 0039 line 4);
concatenating, by the at least one processor, the first vector from the auto-encoder and the second vector from the neural network to obtain an output vector (“The feature vector 714 and the latent space 708 are concatenated to form one feature vector 716,” paragraph 0042 lines 1-2); and
determining, by the at least one processor and using a layer of the machine learning model (“classifier 718,” paragraph 0042 line 3), a metric (“The output of the deep neural network from processing the multi-dimensional input matrix corresponds to the prediction about the heart condition of the patient,” paragraph 0004 lines 14-17) based on the output vector (“one feature vector 716, which is used as input to a classifier 718,” paragraph 0042 lines 2-3), wherein the machine learning model is a reactive model that decreases an error in predicting an average of metrics, and wherein each metric corresponds to a respective set of features (see 35 U.S.C. § 112(b) rejections).
Jain does not appear to expressly teach a method wherein the neural network is a linear neural network, the auto-encoder is a variational auto-encoder, and the metric is determined using a fully connected layer.
Ozay teaches a method wherein the neural network is a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” paragraph 0024 lines 1-2), the auto-encoder is a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” paragraph 0025 lines 1-2), and the metric is determined using a fully connected layer (figure 1A part 106).
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 the machine learning model of Jain to comprise the linear neural network, the a variational auto-encoder, and the fully connected layer of Ozay. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely implementing the machine learning model using a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” Ozay paragraph 0024 lines 1-2), a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” Ozay paragraph 0025 lines 1-2), and a fully connected layer (Ozay figure 1A part 106). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method wherein determining the second vector further comprises:
determining a mean value and a standard deviation (“we model the distribution of revived evanescent representations by p(F∈ℱold0))∝p(F∈ℱoldn:𝜋c⊢Пoldt,0)~N(𝜋c,σc), that is, the distribution p(F∈ℱold0)) is approximated by the empirical model p(F∈ℱoldn:𝜋c∈Пoldt,0) identified by prototypes 𝜋c∈Пoldt,0 which is estimated by a Gaussian model N(𝜋c,σc) with mean set to the prototype 𝜋c and with the standard deviation σc,” Ozay paragraph 0139 lines 12-16) associated with a latent space of the variational auto-encoder (“To identify and train the feature drift and semantic drift models Γγ and ΨΨ, two different core architectures are proposed. … The second core architecture which may be used is a variational model (VM) which comprises a conditional variational encoder (VAE) having an encoder and a decoder separated by a latent space,” Ozay paragraph 0131 lines 2-10); and
generating a latent representation (“To identify and train the feature drift and semantic drift models Γγ and ΨΨ, two different core architectures are proposed. … The second core architecture which may be used is a variational model (VM) which comprises a conditional variational encoder (VAE) having an encoder and a decoder separated by a latent space,” Ozay paragraph 0131 lines 2-10) based on the mean and the standard deviation (“we model the distribution of revived evanescent representations by p(F∈ℱold0))∝p(F∈ℱoldn:𝜋c⊢Пoldt,0)~N(𝜋c,σc), that is, the distribution p(F∈ℱold0)) is approximated by the empirical model p(F∈ℱoldn:𝜋c∈Пoldt,0) identified by prototypes 𝜋c∈Пoldt,0 which is estimated by a Gaussian model N(𝜋c,σc) with mean set to the prototype 𝜋c and with the standard deviation σc,” Ozay paragraph 0139 lines 12-16), wherein the second vector corresponds to the latent representation (“The output of the model is the set of features for the revived evanescent representations, i.e. ℱoldn,” Ozay paragraph 0139 lines 21-23).
As to dependent claim 3, the rejection of claim 2 is incorporated. Jain/Ozay further teaches a method wherein the latent representation is generated using a normal distribution based on the mean and the standard deviation (“The learning objective may thus be composed of a reconstruction constraint and a regularisation term measuring the KL divergence between the posterior distribution modelled by the encoder and the standard normal prior, plus an additional term to maximise the mutual information between input and latent variables,” Ozay paragraph 0154 lines 3-8).
As to dependent claim 4, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method comprising simultaneously training the linear neural network and the variational auto-encoder using a backpropagation technique (“Gradient backpropagation may be used for training both models,” Ozay paragraph 0167 lines 19-20).
As to dependent claim 5, the rejection of claim 4 is incorporated. Jain/Ozay further teaches a method wherein the training further comprises:
using a Kullback-leibler (KL) divergence and mean square error as a reconstruction component of loss function for the variational auto-encoder (“the training may be done by optimizing the variational model (conditional VAE) by maximizing the Evidence Lower Bound (ELBO) as proposed in [5]. The learning objective may thus be composed of a reconstruction constraint and a regularisation term measuring the KL divergence between the posterior distribution modelled by the encoder and the standard normal prior. The loss function Lst for semantic drift may be expressed as: Ls(Ft0,Пoldt,0;Ψtn)==Lrec,s+λkldLkld,s where Lrec,st is the reconstruction loss, Lkl,st is the KL divergence loss and λkld,s is a constant set to 1 in all experiments, i.e. for the CIFAR100, TinyImageNet and CUB200 datasets,” Ozay paragraph 0156 lines 2-14; “the training may be done by minimising an error, e.g. the mean squared error,” Ozay paragraph 0153 lines 4-5); and
using a binary cross-entropy as the loss function for the fully-connected layer (“The classification model may be trained using a cross-entropy loss,” Ozay paragraph 0039 lines 1-2).
As to dependent claim 8, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method wherein a size of an input of the fully connected layer corresponds to a size of the output vector (Ozay figure 1A – compare parts 104 and 106).
As to dependent claim 9, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method wherein the variational auto-encoder models a probability distribution of a latent variable of the set of features based on a normal distribution (“The learning objective may thus be composed of a reconstruction constraint and a regularisation term measuring the KL divergence between the posterior distribution modelled by the encoder and the standard normal prior, plus an additional term to maximise the mutual information between input and latent variables,” Ozay paragraph 0154 lines 3-8).
As to dependent claim 10, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method wherein the variational auto-encoder comprises a plurality of fully-connected layers (“the MLP is composed of two fully-connected (FC) layers,” Ozay paragraph 0132 lines 9-10).
As to independent claim 11, Jain teaches a system, comprising:
a memory (“computer readable media, e.g., RAM, ROM, secondary storage, etc.,” paragraph 0069 lines 8-9); and
at least one processor (“one or more processor cores,” paragraph 0006 lines 2-3) coupled to the memory and configured to:
input (“A biosignal 701 is input and processed,” paragraph 0038 line 1) a set of features into a machine learning model, wherein the machine learning model comprises a neural network (“The DenseNet 712 shown in FIG. 7 may be implemented with a form of a deep neural network 712,” paragraph 0046 lines 1-2) and a auto-encoder (“The encoder 704 and decoder 706 form an autoencoder 702,” paragraph 0044 lines 1-2);
determine, using the neural network, a first vector based on the set of features (“The tensorform biosignal 710 is input into a Dense network 712 to produce a feature vector 714,” paragraph 0041 lines 1-2);
determine, using the auto-encoder, a second vector based on the set of features (“A biosignal 701 is input and processed through an encoder 704 which performs a lossy compression. The minimized representation of this lossy compression, called the latent space 708, serves as input to two layers: a concatenation layer 716 (used later on),” paragraph 0038 line 1 to paragraph 0039 line 4);
concatenate the first vector from the auto-encoder and the second vector from the neural network to obtain an output vector (“The feature vector 714 and the latent space 708 are concatenated to form one feature vector 716,” paragraph 0042 lines 1-2); and
determine, using a layer of the machine learning model (“classifier 718,” paragraph 0042 line 3), a metric (“The output of the deep neural network from processing the multi-dimensional input matrix corresponds to the prediction about the heart condition of the patient,” paragraph 0004 lines 14-17) based on the output vector (“one feature vector 716, which is used as input to a classifier 718,” paragraph 0042 lines 2-3).
Jain does not appear to expressly teach a method wherein the neural network is a linear neural network, the auto-encoder is a variational auto-encoder, and the metric is determined using a fully connected layer.
Ozay teaches a method wherein the neural network is a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” paragraph 0024 lines 1-2), the auto-encoder is a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” paragraph 0025 lines 1-2), and the metric is determined using a fully connected layer (figure 1A part 106).
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 the machine learning model of Jain to comprise the linear neural network, the a variational auto-encoder, and the fully connected layer of Ozay. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely implementing the machine learning model using a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” Ozay paragraph 0024 lines 1-2), a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” Ozay paragraph 0025 lines 1-2), and a fully connected layer (Ozay figure 1A part 106). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 12, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein the at least one processor is further configured to:
determine a mean value and a standard deviation (“we model the distribution of revived evanescent representations by p(F∈ℱold0))∝p(F∈ℱoldn:𝜋c⊢Пoldt,0)~N(𝜋c,σc), that is, the distribution p(F∈ℱold0)) is approximated by the empirical model p(F∈ℱoldn:𝜋c∈Пoldt,0) identified by prototypes 𝜋c∈Пoldt,0 which is estimated by a Gaussian model N(𝜋c,σc) with mean set to the prototype 𝜋c and with the standard deviation σc,” Ozay paragraph 0139 lines 12-16) associated with a latent space of the variational auto-encoder (“To identify and train the feature drift and semantic drift models Γγ and ΨΨ, two different core architectures are proposed. … The second core architecture which may be used is a variational model (VM) which comprises a conditional variational encoder (VAE) having an encoder and a decoder separated by a latent space,” Ozay paragraph 0131 lines 2-10); and
generate a latent representation (“To identify and train the feature drift and semantic drift models Γγ and ΨΨ, two different core architectures are proposed. … The second core architecture which may be used is a variational model (VM) which comprises a conditional variational encoder (VAE) having an encoder and a decoder separated by a latent space,” Ozay paragraph 0131 lines 2-10) of the set of features based on the mean and the standard deviation (“we model the distribution of revived evanescent representations by p(F∈ℱold0))∝p(F∈ℱoldn:𝜋c⊢Пoldt,0)~N(𝜋c,σc), that is, the distribution p(F∈ℱold0)) is approximated by the empirical model p(F∈ℱoldn:𝜋c∈Пoldt,0) identified by prototypes 𝜋c∈Пoldt,0 which is estimated by a Gaussian model N(𝜋c,σc) with mean set to the prototype 𝜋c and with the standard deviation σc,” Ozay paragraph 0139 lines 12-16), wherein the second vector corresponds to the latent representation (“The output of the model is the set of features for the revived evanescent representations, i.e. ℱoldn,” Ozay paragraph 0139 lines 21-23).
As to dependent claim 13, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein the at least one processor is further configured to simultaneously train the linear neural network and the variational auto-encoder using a backpropagation technique (“Gradient backpropagation may be used for training both models,” Ozay paragraph 0167 lines 19-20).
As to dependent claim 14, the rejection of claim 13 is incorporated. Jain/Ozay further teaches a system wherein the at least one processor is further configured to:
use a Kullback-leibler (KL) divergence and mean squared error as a reconstruction component as a loss function for the variational auto-encoder (“the training may be done by optimizing the variational model (conditional VAE) by maximizing the Evidence Lower Bound (ELBO) as proposed in [5]. The learning objective may thus be composed of a reconstruction constraint and a regularisation term measuring the KL divergence between the posterior distribution modelled by the encoder and the standard normal prior. The loss function Lst for semantic drift may be expressed as: Ls(Ft0,Пoldt,0;Ψtn)==Lrec,s+λkldLkld,s where Lrec,st is the reconstruction loss, Lkl,st is the KL divergence loss and λkld,s is a constant set to 1 in all experiments, i.e. for the CIFAR100, TinyImageNet and CUB200 datasets,” Ozay paragraph 0156 lines 2-14; “the training may be done by minimising an error, e.g. the mean squared error,” Ozay paragraph 0153 lines 4-5); and
use a binary cross-entropy as the loss function for the fully-connected layer (“The classification model may be trained using a cross-entropy loss,” Ozay paragraph 0039 lines 1-2).
As to dependent claim 17, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein a size of an input of the fully connected layer corresponds to a size of the output vector (Ozay figure 1A – compare parts 104 and 106).
As to dependent claim 18, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein the variational auto-encoder models a probability distribution of a latent variable of the set of features based on a normal distribution (“The learning objective may thus be composed of a reconstruction constraint and a regularisation term measuring the KL divergence between the posterior distribution modelled by the encoder and the standard normal prior, plus an additional term to maximise the mutual information between input and latent variables,” Ozay paragraph 0154 lines 3-8).
As to dependent claim 19, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein the variational auto-encoder comprises a plurality of fully-connected layers (“the MLP is composed of two fully-connected (FC) layers,” Ozay paragraph 0132 lines 9-10).
As to independent claim 20, Jain teaches a non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations (“software modules stored or otherwise maintained in computer readable media, e.g., RAM, ROM, secondary storage, etc. One or more processing cores (e.g., CPU or GPU cores) of the machine learning system may then execute the software modules to implement the function of the respective machine learning system,” paragraph 0069 lines 7-12) comprising:
inputting (“A biosignal 701 is input and processed,” paragraph 0038 line 1) a set of features into a machine learning model, wherein the machine learning model comprises a neural network (“The DenseNet 712 shown in FIG. 7 may be implemented with a form of a deep neural network 712,” paragraph 0046 lines 1-2) and an auto-encoder (“The encoder 704 and decoder 706 form an autoencoder 702,” paragraph 0044 lines 1-2);
determining, using the neural network, a first vector based on the set of features (“The tensorform biosignal 710 is input into a Dense network 712 to produce a feature vector 714,” paragraph 0041 lines 1-2);
determining, using the auto-encoder, a second vector based on the set of features (“A biosignal 701 is input and processed through an encoder 704 which performs a lossy compression. The minimized representation of this lossy compression, called the latent space 708, serves as input to two layers: a concatenation layer 716 (used later on),” paragraph 0038 line 1 to paragraph 0039 line 4);
concatenating the first vector from the auto-encoder and the second vector from the neural network to obtain an output vector (“The feature vector 714 and the latent space 708 are concatenated to form one feature vector 716,” paragraph 0042 lines 1-2); and
determining, using a layer of the machine learning model (“classifier 718,” paragraph 0042 line 3), a metric (“The output of the deep neural network from processing the multi-dimensional input matrix corresponds to the prediction about the heart condition of the patient,” paragraph 0004 lines 14-17) based on the output vector (“one feature vector 716, which is used as input to a classifier 718,” paragraph 0042 lines 2-3).
Jain does not appear to expressly teach a method wherein the neural network is a linear neural network, the auto-encoder is a variational auto-encoder, and the metric is determined using a fully connected layer.
Ozay teaches a method wherein the neural network is a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” paragraph 0024 lines 1-2), the auto-encoder is a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” paragraph 0025 lines 1-2), and the metric is determined using a fully connected layer (figure 1A part 106).
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 the machine learning model of Jain to comprise the linear neural network, the a variational auto-encoder, and the fully connected layer of Ozay. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely implementing the machine learning model using a linear neural network (“The feature drift model may use a linear deep neural network (DNN),” Ozay paragraph 0024 lines 1-2), a variational auto-encoder (“The feature drift model may use a variational model such as a variational auto-encoder (VAE),” Ozay paragraph 0025 lines 1-2), and a fully connected layer (Ozay figure 1A part 106). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Claims 6 and 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Jain in view of Ozay and Valpola (US 2020/0151547 A1).
As to dependent claim 6, the rejection of claim 1 is incorporated. Jain/Ozay further teaches a method wherein the metric is a first metric (“The output of the deep neural network from processing the multi-dimensional input matrix corresponds to the prediction about the heart condition of the patient,” Jain paragraph 0004 lines 14-17) and the machine learning model is a first machine learning model network (“The DenseNet 712 shown in FIG. 7 may be implemented with a form of a deep neural network 712,” Jain paragraph 0046 lines 1-2).
Jain/Ozay does not appear to expressly teach a method wherein the method further comprises providing the first metric as an input to a second machine learning model, wherein the second machine learning model receives the set of features and the first metric as inputs and outputs a second metric, the second metric being another prediction of the first metric.
Valpola teaches a method wherein the method further comprises providing the first metric as an input to a second machine learning model, wherein the second machine learning model receives the set of features and the first metric as inputs and outputs a second metric, the second metric being another prediction of the first metric (“The second machine learning model component of the machine learning system may be trained to generate the calibrated uncertainty estimate of the prediction in response to a receipt, as an input to the second machine learning component, the following: the prediction; the uncertainty estimate of the prediction; and an output of at least one anomaly detector,” paragraph 0017 lines 1-7).
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 the first model of Jain/Ozay to comprise the second model of Valpola. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calibrating uncertainty of the first metric (“The second machine learning model component of the machine learning system may be trained to generate the calibrated uncertainty estimate of the prediction in response to a receipt, as an input to the second machine learning component, the following: the prediction; the uncertainty estimate of the prediction; and an output of at least one anomaly detector,” Valpola paragraph 0017 lines 1-7). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 15, the rejection of claim 11 is incorporated. Jain/Ozay further teaches a system wherein the metric is a first metric (“The output of the deep neural network from processing the multi-dimensional input matrix corresponds to the prediction about the heart condition of the patient,” Jain paragraph 0004 lines 14-17) and the machine learning model is a first machine learning model network (“The DenseNet 712 shown in FIG. 7 may be implemented with a form of a deep neural network 712,” Jain paragraph 0046 lines 1-2).
Jain/Ozay does not appear to expressly teach a system wherein the at least one processor is further configured to provide the first metric as an input to a second machine learning model, wherein the second machine learning model receives the set of features and the first metric as inputs and outputs a second metric.
Valpola teaches a system wherein the at least one processor is further configured to provide the first metric as an input to a second machine learning model, wherein the second machine learning model receives the set of features and the first metric as inputs and outputs a second metric (“The second machine learning model component of the machine learning system may be trained to generate the calibrated uncertainty estimate of the prediction in response to a receipt, as an input to the second machine learning component, the following: the prediction; the uncertainty estimate of the prediction; and an output of at least one anomaly detector,” paragraph 0017 lines 1-7).
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 the first model of Jain/Ozay to comprise the second model of Valpola. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calibrating uncertainty of the first metric (“The second machine learning model component of the machine learning system may be trained to generate the calibrated uncertainty estimate of the prediction in response to a receipt, as an input to the second machine learning component, the following: the prediction; the uncertainty estimate of the prediction; and an output of at least one anomaly detector,” Valpola paragraph 0017 lines 1-7). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Claims 7 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Jain in view of Ozay and Kemka et al. (US 2024/0070552 A1, hereinafter Kemka).
As to dependent claim 7, the rejection of claim 1 is incorporated.
Jain/Ozay does not appear to expressly teach a method wherein the metric is a prediction of a default rate of a customer.
Kemka teaches a method wherein the metric is a prediction of a default rate of a customer (“predict when an invoice that is due but not yet overdue (or late), will be paid,” paragraph 0073 lines 1-3).
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 the prediction of Jain/Ozay to comprise the default rate of Kemka. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely predicting default rates (“predict when an invoice that is due but not yet overdue (or late), will be paid,” Kemka paragraph 0073 lines 1-3). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 16, the rejection of claim 11 is incorporated.
Jain/Ozay does not appear to expressly teach a system wherein the metric is a prediction of a default rate of a customer.
Kemka teaches a system wherein the metric is a prediction of a default rate of a customer (“predict when an invoice that is due but not yet overdue (or late), will be paid,” paragraph 0073 lines 1-3).
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 the prediction of Jain/Ozay to comprise the default rate of Kemka. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely predicting default rates (“predict when an invoice that is due but not yet overdue (or late), will be paid,” Kemka paragraph 0073 lines 1-3). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2021/0334908 A1 disclosing an autoencoder and a neural network producing output vectors in parallel which are then concatenated and used to determine a metric
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights