DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner believes that the title of the invention is imprecise. A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See MPEP 606.01. However, the title of the invention should be limited to 500 characters. Examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Claim Interpretation
Method claims 7 and 20 each recite the following contingent limitation(s): transforming the training data in a form of a matrix to an input in a form of a low-dimensional vector for the proxy Gaussian process model when the training data includes image data. The limitation(s) is/are contingent because they recite the contingent phrase when the training data includes image data. The broadest reasonable interpretation of the claim requires the training data to not include image data. Since it can be interpreted to not activate one (or more) condition(s), the method claim thereby represents a broader scope than other identical claims from different statutory categories. See MPEP 2111.04(II) for more information.
Method claim 9 recites the following contingent limitation(s): integrating output of a plurality of categories of the deterministic ANN model into one scalar when an output layer of the deterministic ANN model includes a plurality of units. The limitation(s) is/are contingent because it recites the contingent phrase when an output layer of the deterministic ANN model includes a plurality of units. The broadest reasonable interpretation of the claim requires the output layer to not include a plurality of units. Since it can be interpreted to not activate one (or more) condition(s), the method claim thereby represents a broader scope than other identical claims from different statutory categories. See MPEP 2111.04(II) for more information.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Exemplary claim 5 recites wherein the generating of the dataset comprises generating a second training dataset that includes the output of the trained deterministic ANN model by matching the same with the training data as a temporary output label. The term the same lacks clear antecedent basis. For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a method claim. Claim 13 is a CRM claim. Claim 14 is a device claim. Therefore, claims 1, 13, and 14 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
generating, by the at least one processor, a dataset by combining training data used for training of a deterministic artificial neural network (ANN) model and output of the deterministic ANN model trained with the training data (mental process – user can manually generate a dataset by combining training data used for training of a deterministic artificial neural network (ANN) model and output of the deterministic ANN model trained with the training data)
estimating, by the at least one processor, output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset (mental process – user can manually estimate output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
by the at least one processor (mere instructions to apply the exception using a generic computer component)
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
by the at least one processor (mere instructions to apply the exception using a generic computer component)
Conclusion: The claim is not patent eligible.
Claims 13 and 14 are rejected on the same grounds as claim 1. Additionally for claims 13 and 14: Claim 13 has the additional elements of a non-transitory computer-readable medium storing instructions. These elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B. Claim 14 has the additional elements of at least one processor configured to execute computer-readable instructions. These elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claim 2: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the estimating comprises estimating a predictive variance output from the proxy Gaussian process model as the output uncertainty of the deterministic ANN model.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 3: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the variance is determined through approximation to the output uncertainty of the deterministic ANN model based on equivalence between a Gaussian process model and a probabilistic neural network model and a Bayesian interpretation of a kernel ridge regression (KRR) algorithm.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 4: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of training, by the at least one processor, the deterministic ANN model using a first training dataset that includes the training data and an answer label corresponding to the training data.
These judicial exceptions are not integrated into a practical application. The additional element(s) of training, by the at least one processor, the deterministic ANN model using a first training dataset that includes the training data and an answer label corresponding to the training data recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of training, by the at least one processor, the deterministic ANN model using a first training dataset that includes the training data and an answer label corresponding to the training data recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 5: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the generating of the dataset comprises generating a second training dataset that includes the output of the trained deterministic ANN model by matching the same with the training data as a temporary output label.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 6: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of generating, by the at least one processor, the proxy Gaussian process model by training a Gaussian process model with the generated dataset.
These judicial exceptions are not integrated into a practical application. The additional element(s) of by the at least one processor are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The additional element(s) of generating, by the at least one processor, the proxy Gaussian process model by training a Gaussian process model with the generated dataset recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of by the at least one processor amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. The additional element(s) of generating, by the at least one processor, the proxy Gaussian process model by training a Gaussian process model with the generated dataset recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 7: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the generating of the dataset comprises transforming the training data in a form of a matrix to an input in a form of a low-dimensional vector for the proxy Gaussian process model when the training data includes image data.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 8: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the transforming to the input comprises transforming the training data for training the proxy Gaussian process model to a feature vector extracted from an intermediate hidden layer of the deterministic ANN model for the training data.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 9: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the generating of the dataset comprises integrating output of a plurality of categories of the deterministic ANN model into one scalar when an output layer of the deterministic ANN model includes a plurality of units.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 10: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the integrating comprises integrating, into one scalar, the output of the plurality of categories of the deterministic ANN model by transforming the output of the deterministic ANN model from a vector expressed through a softmax function to entropy that is a one-dimensional unit value.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 11: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the deterministic ANN model includes at least one of a classification neural network, a regression neural network, and a generative model.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 12: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually use wherein the estimating comprises estimating the output uncertainty of the deterministic ANN model based on the output for the test data of the proxy Gaussian process model without modifying a structure of the deterministic ANN model.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Claims 15- 20 are rejected on the same grounds as claims 2-7 respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-7, 9-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vijaykeerthy et al. (hereinafter Vijaykeerthy), U.S. Patent Application Publication 2024/0185027 in view of Nartey et al. (hereinafter Nartey), Semi-Supervised Learning for Fine-Grained Classification With Self-Training.
Regarding Claim 1, Vijaykeerthy disclose an output uncertainty estimation method of a computer device including at least one processor, the output uncertainty estimation method comprising:
generating, by the at least one processor, a dataset by combining training data used for training of a deterministic artificial neural network (ANN) model [“The model to be tested is referred to herein as a target model” ¶17; “receives a set of target model training data. Target model training data is data used to train the target model, and (like testing data) includes incudes (sic) data samples of the type the target model is trained to correctly process, along with a label for each data sample indicating the correct decision for a data sample.” ¶17; “uses a portion of the trained target model to generate the encoded representation. For example, an input stage of a trained neural network model is often configured to generate a numerical representation of input to the model” ¶18] and output of the deterministic ANN model trained with the training data; and
estimating, by the at least one processor, output uncertainty of the deterministic ANN model based on output for test data of a proxy Gaussian process model trained through the generated dataset [“to train a proxy model to determine an uncertainty score corresponding to an output of a trained target model, uses the trained proxy model to compute a set of uncertainty scores corresponding to portions of target model testing data” ¶16; “One embodiment uses a Bayesian model, such as a Gaussian Process, to train the proxy model.” ¶19; “uses the trained proxy model to compute a mean and variance for an encoded representation of a data sample in the target model testing data, and converts the mean and variance to a corresponding uncertainty score” ¶20].
However, Vijaykeerthy fails to explicitly disclose and output of the deterministic ANN model trained with the training data.
Nartey discloses and output of the deterministic ANN model trained with the training data [“a model is trained with a set of labeled data samples, followed by prediction on the unlabeled data and then a selection of the unlabeled data with high confidence to be incrementally appended to the labeled training data with their predicted labels. It is a technique that leverages a supervised model to generate pseudo-labels for unlabeled data samples and add the samples that are selected with the highest confidence to the training data together with their generated pseudo-labels, thereby enlarging the training data size. This procedure is repeated until the model converges.” §II.B ¶1; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Vijaykeerthy and Nartey before him before the effective filing date of the claimed invention, to modify the method of Vijaykeerthy to incorporate the use of model output as part of the dataset of Nartey.
Given the advantage of increasing dataset size for improved accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses wherein the estimating comprises estimating a predictive variance output from the proxy Gaussian process model as the output uncertainty of the deterministic ANN model [“a proxy model, because it is a proxy for uncertainty in the target model's results…Variance is the expectation of the squared deviation of a random variable from its mean, and thus variance is a measure of how far a set of numbers is spread out from their mean. One embodiment uses a Bayesian model, such as a Gaussian Process, to train the proxy model.” ¶19].
Regarding Claim 4, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses training, by the at least one processor, the deterministic ANN model using a first training dataset that includes the training data and an answer label corresponding to the training data [“Target model training data is data used to train the target model, and (like testing data) includes incudes data samples of the type the target model is trained to correctly process, along with a label for each data sample indicating the correct decision for a data sample” ¶17].
Regarding Claim 5, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 4.
However, Vijaykeerthy fails to explicitly disclose wherein the generating of the dataset comprises generating a second training dataset that includes the output of the trained deterministic ANN model by matching the same with the training data as a temporary output label.
Nartey discloses wherein the generating of the dataset comprises generating a second training dataset that includes the output of the trained deterministic ANN model by matching the same with the training data as a temporary output label [“a model is trained with a set of labeled data samples, followed by prediction on the unlabeled data and then a selection of the unlabeled data with high confidence to be incrementally appended to the labeled training data with their predicted labels. It is a technique that leverages a supervised model to generate pseudo-labels for unlabeled data samples and add the samples that are selected with the highest confidence to the training data together with their generated pseudo-labels, thereby enlarging the training data size. This procedure is repeated until the model converges.” §II.B ¶1; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Vijaykeerthy and Nartey before him before the effective filing date of the claimed invention, to modify the combination to incorporate semi-supervised training of Nartey.
Given the advantage of increasing dataset size for improved accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 6, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses generating, by the at least one processor, the proxy Gaussian process model by training a Gaussian process model with the generated dataset [“to train a proxy model to determine an uncertainty score corresponding to an output of a trained target model, uses the trained proxy model to compute a set of uncertainty scores corresponding to portions of target model testing data” ¶16; “One embodiment uses a Bayesian model, such as a Gaussian Process, to train the proxy model.” ¶19].
Regarding Claim 7, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses wherein the generating of the dataset comprises transforming the training data in a form of a matrix to an input in a form of a low-dimensional vector for the proxy Gaussian process model when the training data includes image data [“As another example, a residual neural network (ResNet) is a presently available neural network model for computing encoded numerical representations of images.” ¶18].
Regarding Claim 9, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses wherein the generating of the dataset comprises integrating output of a plurality of categories of the deterministic ANN model into one scalar [“uses the trained proxy model to compute a mean and variance for an encoded representation of a data sample in the target model testing data, and converts the mean and variance to a corresponding uncertainty score.” ¶20] when an output layer of the deterministic ANN model includes a plurality of units [“classify images of animals” ¶12].
Regarding Claim 10, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 9. Vijaykeerthy further discloses wherein the integrating comprises integrating, into one scalar, the output of the plurality of categories of the deterministic ANN model by transforming the output of the deterministic ANN model from a vector expressed through a softmax function to entropy that is a one-dimensional unit value [determine an uncertainty score corresponding to an output of a trained target model, uses the trained proxy model to compute a set of uncertainty scores corresponding to portions of target model testing data” ¶16; Examiner Note: While not defined in the original disclosure, a person having ordinary skill in the art understands that entropy is a level of uncertainty of a model].
However, Vijaykeerthy fails to explicitly disclose from a vector expressed through a softmax function.
Nartey discloses from a vector expressed through a softmax function [“a semi-supervised model with softmax output” §III.A ¶2; “the softmax output containing the class probabilities” §III.A ¶2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Vijaykeerthy and Nartey before him before the effective filing date of the claimed invention, to modify the combination to incorporate the well-known softmax function of Nartey.
Given the advantage of performing the widely-used softmax function to normalize the output to a probability distribution over the predicted classes, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 11, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses wherein the deterministic ANN model includes at least one of a classification neural network, a regression neural network, and a generative model [“For example, for a model being trained to classify images” ¶2; “a trained neural network model” ¶18].
Regarding Claim 12, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 1. Vijaykeerthy further discloses wherein the estimating comprises estimating the output uncertainty of the deterministic ANN model based on the output for the test data of the proxy Gaussian process model without modifying a structure of the deterministic ANN model [“uses the trained proxy model to compute a mean and variance for an encoded representation of a data sample in the target model testing data, and converts the mean and variance to a corresponding uncertainty score” ¶20; “One embodiment uses a Bayesian model, such as a Gaussian Process, to train the proxy model.” ¶19].
Claim 13 is rejected on the same grounds as claim 1.
Claim 14-15, 17-20 is rejected on the same grounds as claim 1-2, 4-7 respectively.
Claim(s) 3 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vijaykeerthy and Nartey, in view of Varnek et al. (hereinafter Varnek), Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
Regarding Claim 3, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 2. Vijaykeerthy further discloses wherein the variance is determined through approximation to the output uncertainty of the deterministic ANN model [“uses the trained proxy model to compute a mean and variance for an encoded representation of a data sample in the target model testing data, and converts the mean and variance to a corresponding uncertainty score” ¶20] based on equivalence between a Gaussian process model and a probabilistic neural network model and a Bayesian interpretation of a kernel ridge regression (KRR) algorithm.
However, Vijaykeerthy fails to explicitly disclose based on equivalence between a Gaussian process model and a probabilistic neural network model and a Bayesian interpretation of a kernel ridge regression (KRR) algorithm.
Varnek discloses based on equivalence between a Gaussian process model and a probabilistic neural network model and a Bayesian interpretation of a kernel ridge regression (KRR) algorithm [“As an example, the mean predictor of the Gaussian processes regression59 (Bayesian) exactly coincides with the solution provided by kernel ridge regression (frequentist). The most popular machine learning methods involving Bayesian learning are Bayesian regression,50 Bayesian neural networks,50,60 and Gaussian processes.59 The advantages of Bayesian learning algorithms have been demonstrated in recent QSAR studies involving Bayesian neural networks61−64 and Gaussian processes.65−67” pg. 6, col. 1, lines 2-11].
It would have been obvious to one having ordinary skill in the art, having the teachings of Vijaykeerthy, Nartey, and Varnek before him before the effective filing date of the claimed invention, to modify the combination to incorporate variance determination through using various related models of Varnek.
Given the advantage of ensured accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 16 is rejected on the same grounds as claim 3.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vijaykeerthy and Nartey, in view of Lee et al. (hereinafter Lee), Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes.
Regarding Claim 8, Vijaykeerthy and Nartey disclose the output uncertainty estimation method of claim 7. Vijaykeerthy further discloses wherein the transforming to the input comprises transforming the training data for training the proxy Gaussian process model to a feature vector extracted from an intermediate hidden layer of the deterministic ANN model for the training data [“generates an encoded representation of a data sample in the set of target model training data” ¶18].
However, Vijaykeerthy fails to explicitly disclose extracted from an intermediate hidden layer of the deterministic ANN model.
Lee discloses extracted from an intermediate hidden layer of the deterministic ANN model [“Neural Linear Models (NLMs)” §3.1 ¶3; “The NLMs can be thought as a Bayesian linear model with learned features from a DNN, which is obtained via a linearization around the DNNs’ last layer.” §3.1 ¶4; Examiner Note: Neural Linear Models have a hybrid architecture that begin with a neural network and end with a Bayesian model (e.g., Gaussian process). Since the neural network does not have a traditional output layer, the final intermediate hidden layer of the neural network passes the data to the Gaussian process for transformation].
It would have been obvious to one having ordinary skill in the art, having the teachings of Vijaykeerthy, Nartey, and Lee before him before the effective filing date of the claimed invention, to modify the combination to incorporate extracting from an intermediate layer of Lee.
Given the advantage of producing accuracy uncertainty metrics while being computationally less than a full Bayesian neural network, one having ordinary skill in the art would have been motivated to make this obvious modification.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
The following references were found during the examination of this patent application and were found to be relevant to patentability. Applicant is advised to review these references prior to responding to this Office action.
Liu et al. (Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness) discloses a Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs, by adding a weight normalization step during training and replacing the output layer with a Gaussian Process.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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