DETAILED ACTION
This action is in response to the amendment filed 04/14/2026. Claims 1-2, 4-5, 7, 9, 12, 15, 23, 28, 30-31, and 37-44 are pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement filed 08/31/2022 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the document cited as “Kaggle, House Prices: Advanced Regression Techniques. Accessed online on August 30, 2022 at: https://www.kaggle.com/c/house-prices-advanced-regressiontechniques” is not legible. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
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 1-2, 4-5, 7, 9, 12, 15, 23, 28, 30-31, and 37-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
generating a synthetic training dataset (This limitation is a mental process as it encompasses a human mentally creating a dataset.)
selecting a feature ci of the original training dataset as a target vector yi (This limitation is a mental process as it encompasses a human mentally selecting a feature as a target vector.)
selecting remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the original training dataset other than a feature corresponding to the selected feature ci (This limitation is a mental process as it encompasses a human mentally selecting features as vectors.)
generating an estimate y'i of the target vector yi (This limitation is a mental process as it encompasses a human mentally creating an estimate.)
inserting a synthetic feature c'i corresponding to the estimate y'i of the target vector yi into the synthetic training dataset (This limitation is a mental process as it encompasses a human mentally inserting a feature into a dataset.)
appending the synthetic training dataset to the original training dataset to form a hybrid training dataset; (This limitation is a mental process as it encompasses a human mentally combining datasets.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
for training a machine learning model using an original training dataset including a plurality of features, (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
training a prediction model f(yi | X\i) (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
by applying the prediction model to the set of training vectors X\i; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
training the machine learning model using the hybrid training dataset (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model using a dataset (see MPEP 2106.05(g)).)
transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
for training a machine learning model using an original training dataset including a plurality of features specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
training a prediction model f(yi | X\i) is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
by applying the prediction model to the set of training vectors X\i uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
training the machine learning model using the hybrid training dataset is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
repeating, for a plurality of features of the original training dataset, operations of selecting a feature of the original training dataset, selecting remaining features of the original training dataset (This limitation is a mental process as it encompasses a human mentally repeating operations of selecting features.)
generating the estimate of the target vector and inserting the synthetic feature into the synthetic training dataset. (This limitation is a mental process as it encompasses a human mentally creating an estimate and inserting features into a dataset.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
training the prediction model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
training the model is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites the same abstract ideas as claim 1. Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
wherein the prediction model comprises a bagging or boosting algorithm. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the prediction model comprises a bagging or boosting algorithm specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites the same abstract ideas as claim 1. Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
wherein the prediction model comprises a random forest prediction model or gradient boosting tree model. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the prediction model comprises a random forest prediction model or gradient boosting tree model specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
wherein generating the estimate y'i of the target vector yi comprises: generating an estimate y'i of the target vector yi (This limitation is a mental process as it encompasses a human mentally creating an estimate.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
by applying the prediction model as f(X\i) - > y'i (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
by applying the prediction model as f(X\i) - > y'i uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites the same abstract ideas as claim 1. Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
wherein the machine learning model comprises a neural network. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the machine learning model comprises a neural network specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 9 is subject-matter ineligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites
splitting the preliminary training dataset into the original training dataset and a verification dataset before generating the synthetic training dataset (This limitation is a mental process as it encompasses a human mentally splitting the datasets.)
verifying the machine learning model using the verification dataset (This limitation is a mental process as it encompasses a human mentally verifying the network using a dataset.)
performing feature reduction on the preliminary training dataset before splitting the preliminary training dataset into the original training dataset and the verification dataset; (This limitation is a mental process as it encompasses a human mentally performing feature reduction.)
sorting the preliminary training dataset in descending order according to an importance of the features. (This limitation is a mental process as it encompasses a human mentally sorting the dataset.)
Therefore, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of
providing a preliminary training dataset; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because
providing a preliminary training dataset is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 12 is subject-matter ineligible.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
computing a Kullback-Leibler divergence between the original training dataset and the synthetic training dataset to determine a quality of the original training dataset (This limitation is a mental process as it encompasses a human mentally computing a Kullback-Leibler divergence.)
Therefore, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 does not recite additional elements. Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since claim 15 does not recite additional elements, claim 15 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 15 is subject-matter ineligible.
Regarding Claim 23:
Subject Matter Eligibility Analysis Step 1:
Claim 23 recites a computing device and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 23 recites
generates a synthetic training dataset by performing operations (This limitation is a mental process as it encompasses a human mentally creating a dataset.)
selecting a feature ci of the training dataset as a target vector yi, the training dataset comprising a plurality of features (This limitation is a mental process as it encompasses a human mentally selecting a feature as a target vector.)
selecting remaining features of the training dataset as a set of training vectors X\i, where X\i includes all features of the training dataset other than the feature (This limitation is a mental process as it encompasses a human mentally selecting features as vectors.)
generating an estimate y'i of the target vector yi (This limitation is a mental process as it encompasses a human mentally creating an estimate.)
inserting a synthetic feature c'i corresponding to the estimate y'i of the target vector yi into a synthetic training dataset (This limitation is a mental process as it encompasses a human mentally inserting a feature into a dataset.)
appending the synthetic training dataset to the original training dataset to form a hybrid training dataset; (This limitation is a mental process as it encompasses a human mentally combining datasets.)
Therefore, claim 23 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 23 further recites additional elements of
a computing device, comprising: processing circuitry; and memory coupled to the processing circuitry and including instructions that are executable by the processing circuitry to cause the computing device to perform operations (This element does not integrate the abstract idea into a practical application because it recites a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).)
training a prediction model f(yi | X\i) (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
by applying the prediction model to the set of training vectors X\i; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
training the machine learning model using the hybrid training dataset (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model using a dataset (see MPEP 2106.05(g)).)
transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 23 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 23 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a computing device, comprising: processing circuitry; and memory coupled to the processing circuitry and including instructions that are executable by the processing circuitry to cause the computing device to perform operations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
training a prediction model f(yi | X\i) is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
by applying the prediction model to the set of training vectors X\i uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
training the machine learning model using the hybrid training dataset is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 23 is subject-matter ineligible.
Regarding Claim 28:
Subject Matter Eligibility Analysis Step 1:
Claim 28 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 28 recites
generate synthetic training data (This limitation is a mental process as it encompasses a human mentally creating data.)
select a feature ci of the original training dataset as a target vector yi (This limitation is a mental process as it encompasses a human mentally selecting a feature as a target vector.)
select remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the original training dataset other than a feature corresponding to the selected feature ci (This limitation is a mental process as it encompasses a human mentally selecting features as vectors.)
generate an estimate y'i of the target vector yi (This limitation is a mental process as it encompasses a human mentally creating an estimate.)
insert a synthetic feature c'i corresponding to the estimate y'i of the target vector yi into a synthetic training dataset (This limitation is a mental process as it encompasses a human mentally inserting a feature into a dataset.)
append the synthetic training dataset to the original training dataset to form a hybrid training dataset; (This limitation is a mental process as it encompasses a human mentally combining datasets.)
Therefore, claim 28 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 28 further recites additional elements of
operating a master node in a federated learning system operating a master in a federated learning system (This element does not integrate the abstract idea into a practical application because it recites a generic computer component on which to perform the abstract idea (see MPEP 2106.05(f)).)
a plurality of workers that communicate with the master node(This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
via a message bus (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
transmitting, via the message bus, a message to at least one of the workers instructing the at least one worker to generate synthetic training data by instructing the at least one of the workers(This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
train a prediction model f(yi | X\i) (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
by applying the prediction model to the set of training vectors X\i; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
train a machine learning model using the hybrid training dataset (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model using a dataset (see MPEP 2106.05(g)).)
transmit weights of the machine learning model, trained using the hybrid dataset, to the master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving, via the message bus, model parameters of the machine learning model from the at least one worker that were generated using the synthetic training data (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
wherein the model parameters received from the worker comprise trained neural network weights. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 28 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 28 do not provide significantly more than the abstract idea itself, taken alone and in combination because
operating a master node in a federated learning system operating a master in a federated learning system uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a plurality of workers that communicate with the master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
via a message bus uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
transmitting, via the message bus, a message to at least one of the workers instructing the at least one worker to generate synthetic training data by instructing the at least one of the workers is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
train a prediction model f(yi | X\i) is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
by applying the prediction model to the set of training vectors X\i uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
train the machine learning model using the hybrid training dataset is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
transmit weights of the machine learning model to the master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receiving, via the message bus, model parameters of a machine learning model from the at least one worker that were generated using the synthetic tabular training data is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the model parameters received from the worker comprise trained neural network weights specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 28 is subject-matter ineligible.
Regarding Claim 30:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 30 recites
evaluating the set of preliminary neural network weights; (This limitation is a mental process as it encompasses a human mentally evaluating weights.)
Therefore, claim 30 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 30 further recites additional elements of
receiving from the at least one worker a set of preliminary neural network weights that were trained without using the synthetic training data; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
transmitting the message to the at least one worker instructing the at least one worker to generate the synthetic training data is performed in response to evaluating the set of preliminary neural network weights. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 30 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 30 do not provide significantly more than the abstract idea itself, taken alone and in combination because
receiving from the at least one worker a set of preliminary neural network weights that were trained without using the synthetic training data is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
transmitting the message to the at least one worker instructing the at least one worker to generate the synthetic training data is performed in response to evaluating the set of preliminary neural network weights is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 30 is subject-matter ineligible.
Regarding Claim 31:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 31 recites the same abstract ideas as claim 28. Therefore, claim 31 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 31 further recites additional elements of
after instructing the at least one worker to generate the synthetic training data, receiving a quality metric from the at least one worker (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
wherein the quality metric measures a quality of the synthetic training dataset; (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
instructing the worker to proceed with training a machine learning model using the synthetic training dataset in response to the quality metric. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 31 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 31 do not provide significantly more than the abstract idea itself, taken alone and in combination because
after instructing the at least one worker to generate the synthetic training data, receiving a quality metric from the at least one worker is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the quality metric measures a quality of a synthetic training dataset specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
instructing the worker to proceed with training a machine learning model using the synthetic training dataset in response to the quality metric is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 31 is subject-matter ineligible.
Regarding claim 37, claim 37 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 38, claim 38 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 39, claim 39 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 40, claim 40 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 41, claim 41 recites substantially similar limitations to claim 9, and is therefore rejected under the same analysis.
Regarding claim 42, claim 42 recites substantially similar limitations to claim 10, and is therefore rejected under the same analysis.
Regarding Claim 43:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 43 recites the same abstract ideas as claim 1. Therefore, claim 43 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 43 further recites additional elements of
training a set of weights for a local machine learning model using a real training dataset (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
transmitting the set of weights of the local machine model to the master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving, from the master node, a message with instructions to generate synthetic training data (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 43 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 43 do not provide significantly more than the abstract idea itself, taken alone and in combination because
training a set of weights for a local machine learning model using a real training dataset is the well understood, routine, and conventional activity of training a model based on training data (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “For example, as well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase. The training data provided by the training data collection system 201 can be used to build, train, and/or configure the intention prediction model 173.”)).
transmitting the set of weights of the local machine model to the master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
receiving, from the master node, a message with instructions to generate synthetic training data is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 43 is subject-matter ineligible.
Regarding Claim 44:
Subject Matter Eligibility Analysis Step 1:
Claim 44 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 44 recites
generating a quality metric measuring a quality of the synthetic training dataset (This limitation is a mental process as it encompasses a human mentally generating a quality metric.)
Therefore, claim 44 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 44 further recites additional elements of
transmitting the quality metric to the master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving, from the master node, instructions to proceed with training the machine learning model using the synthetic training dataset (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 44 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 44 do not provide significantly more than the abstract idea itself, taken alone and in combination because
transmitting the quality metric to the master node (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
receiving, from the master node, instructions to proceed with training the machine learning model using the synthetic training dataset is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 44 is subject-matter ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4-5, 7, 9, 23, and 37-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al. (“Improving protein function prediction with synthetic feature samples created by generative adversarial networks”) (hereafter referred to as Wan) in view of Cimentada (“The LOO and the Bootstrap”) (hereafter referred to as Cimentada) in further view of Szeto et al. (US 2018/0018590 A1) (hereafter referred to as Szeto) and Hardy et al. (“MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets”) (hereafter referred to as Hardy).
Regarding claim 1, Wan teaches
A method of generating a synthetic training dataset for training a machine learning model using an original training dataset including a plurality of features (Wan, page 1, abstract, “In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples.” Examiner notes that the protein sequence-based biophysical features are the original dataset and the synthetic training dataset is the synthetic protein feature samples.
appending the synthetic training dataset to the original training dataset to form a hybrid training dataset; and training the machine learning model using the hybrid training dataset (Wan, page 3, 2nd paragraph, “On the last step, FFPred-GAN uses the Classifier Two-Sample Tests (CTST) to select the optimal synthetic training protein feature samples, which are used to augment the original training samples. During the down-stream machine learning classifier training stage, the optimal synthetic samples are expected to derive better classifiers, leading to higher predictive accuracy” where “the SVM trained by the augmented training protein feature samples learning those decision boundaries that successfully separate the protein samples distributed on the right corner of the figure” (Wan, page 9, last paragraph). Examiner notes that the synthetic training dataset is the optimal synthetic training protein feature samples, the training dataset is the original training samples, and the hybrid training dataset is the augmented training protein feature samples. Examiner further notes that the augmenting the datasets is appending the synthetic training dataset to the training dataset to form a hybrid training dataset. Examiner also notes that the SVM is trained by the augmented training protein feature samples or the hybrid training dataset.).
Wan does not teach, but Cimentada does teach
selecting a feature ci of the original training dataset as a target vector yi (Cimentada, page 1, 2nd paragraph, “Let’s imagine a data set with 30 rows. We separate the 1st row to be the test data.” Examiner notes that the first row is the target vector.);
selecting remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the original training dataset other than a feature corresponding to the selected feature ci (Cimentada, page 1, 2nd paragraph, “We separate the 1st row to be the test data and the remaining 29 rows to be the training data.” Examiner notes that the training input vectors are the remaining 29 rows.);
training a prediction model f(yi|X\i) (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that fitting the model is training a prediction model.);
generating an estimate y'i of the target vector yi by applying the prediction model to the set of training vectors X\i (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that predicting the one observation left out is generating an estimate of the target vector.);
Wan and Cimentada are considered analogous to the claimed invention because they both use the Leave One Out method on generated data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan to select features, train a model, and generate an estimate like in Cimentada. Doing so would be advantageous because “it uses all the data. At some point, every rows gets to be the test set and training set, maximizing information. In fact, it uses almost ALL the data as the original data set as the training set is just N-1” (Cimentada, page 2, bullet points).
Wan in view of Cimentada teach the estimate y'i of the target vector yi, but does not teach inserting a synthetic feature corresponding to the estimate into a synthetic training dataset. Szeto does teach
and inserting a synthetic feature c'i corresponding to the estimate y'i …into the synthetic training dataset (Szeto, page 9, paragraph 0018, “The modeling engine further generates one or more private data distributions from the local private data training set where the private data distributions represent the nature of the local private data used to create the trained model. The modeling engine uses the private data distributions to generate a set of proxy data, which can be considered synthetic data or Monte Carlo data having the same general data distribution characteristics as the local private data, while also lacking the actual private or restricted features of the local, private data….The modeling engine then attempts to validate that the set of proxy data is a reasonable training set stand-in for the local, private data by creating a trained proxy model from the set of proxy data” where “From the private data distributions, the machine learning engine can identify or otherwise calculate one or more salient private data features that describe the nature of the private data distributions” (Szeto, page 10, paragraph 0019) and where “private data servers transmit salient features of aggregated private data to a non-private computing devices which in turn creates proxy data for integrating into a trained global model” (Szeto, page 10, paragraph 0026). Examiner notes that the proxy data is the synthetic training dataset. Examiner further notes that the estimate is the salient private data features. By creating proxy data from the salient private data features, the synthetic features correspond to the estimate.)
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada to insert synthetic data into a dataset like in Szeto. Doing so “is considered advantageous because it provides for generating synthetic data capable of reproducing the knowledge gained from private data” (Szeto, page 18, paragraph 0078).
Wan in view of Cimentada and Szeto do not explicitly disclose, but Hardy does disclose
and transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node (Hardy, page 3, 2nd column, 1st paragraph, “Workers perform iterations locally on their data and every E epochs (i.e., each worker passes E times the data in their GAN) they send the resulting parameters to the server” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn” (Hardy, page 3, 2nd column, first two bullet points) Examiner notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further notes that the indication from the master node is the server sending the generated data to the worker. Examiner additionally notes that the server is the master node and the workers transmit parameters or trained weights.)
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Regarding claim 2, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada further teach
repeating, for a plurality of features of the original training dataset, operations of selecting a feature of the original training dataset, selecting remaining features of the original training dataset, training the prediction model, generating the estimate of the target vector (Cimentada, page 1, 2nd paragraph, “LOOCV: Let’s imagine a data set with 30 rows. We separate the 1st row to be the test data and the remaining 29 rows to be the training data. We fit the model on the training data and then predict the one observation we left out. We record the model accuracy and then repeat but predicting the 2nd row from training the model on row 1 and 3:30. We repeat until every row has been predicted.” Examiner notes that repeating until every row has been predicted is repeating for a plurality of features of the original training dataset, operations of selecting a feature of the original training dataset, selecting remaining features of the original training dataset, training the prediction mode, and generating the estimate of the target vector.)
Wan and Cimentada are considered analogous to the claimed invention because they both use the Leave One Out method on generated data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan to repeat the steps of select features, train a model, and generate an estimate like in Cimentada. Doing so would be advantageous because “it uses all the data. At some point, every rows gets to be the test set and training set, maximizing information. In fact, it uses almost ALL the data as the original data set as the training set is just N-1” (Cimentada, page 2, bullet points).
Wan in view of Cimentada teach repeating operations of selecting features, training a model and generating an estimate. Wan in view of Cimentada do not teach repeating the operations of inserting the synthetic feature into the synthetic training dataset, but Szeto does teach
repeating … inserting the synthetic feature into the synthetic training dataset (Szeto, page 24, paragraph 0118, “then the global modeling engine can repeat operations 660 through 680 until a satisfactory similar trained proxy model is generated” where “operation 660 shifts focus from the modeling engine in an entity’s private data server to the non-private computing device’s global modeling engine (see FIG. 1, global modeling engine 136). The global modeling engine receives the salient private data features and locally re-instantiates the private data features distributions in memory. As discussed previously with respect to operation 540, the global modeling engine generates proxy data from the salient private data features, for example, by using the re-instantiated private data distributions as probability distributions to generate new, synthetic sample data” (Szeto, page 23, paragraph 01115). Examiner notes that the repeating operation 660 is repeating inserting the synthetic feature into the synthetic training dataset.)
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada to insert synthetic data into a dataset like in Szeto. Doing so “is considered advantageous because it provides for generating synthetic data capable of reproducing the knowledge gained from private data” (Szeto, page 18, paragraph 0078).
Regarding claim 4, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada and Szeto further teach
wherein the prediction model comprises a bagging or boosting algorithm (Szeto, page 16, paragraph 0069, “More specifically, machine learning algorithms 295 can include implementations of one or more of the following algorithms, … a boosting algorithm…bootstrapped aggregation (bagging).”).
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train prediction models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have implemented the prediction model in Wan in view of Cimentada to use a bagging or boosting algorithm like in Szeto. Thus, this would be applying a known technique (bagging or boosting algorithm) to a known device (prediction model) ready for improvement to yield predictable results (feature prediction) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way).
Regarding claim 5, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada and Szeto further teach
wherein the prediction model comprises a random forest prediction model or gradient boosting tree model (Szeto, page 16, paragraph 0069, “More specifically, machine learning algorithms 295 can include implementations of one or more of the following algorithms, … gradient boosted regression trees (GBRT), a random forest.”).
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train prediction models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have implemented the prediction model in Wan in view of Cimentada to be a random forest prediction model or gradient boosting tree model like in Szeto. Thus, this would be applying a known technique (bagging or boosting algorithm) to a known device (prediction model) ready for improvement to yield predictable results (feature prediction) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way).
Regarding claim 7, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada further teach
wherein generating the estimate y'i of the target vector yi comprises: generating an estimate y'i of the target vector yi by applying the prediction model as f(X\i) - > y'i (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that predicting the one observation left out is generating an estimate of the target vector. Examiner further notes that predicting after fitting the model on the training data is applying the prediction model where the training vectors map to the estimate.).
Wan and Cimentada are considered analogous to the claimed invention because they both use the Leave One Out method on generated data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan to repeat the steps of select features, train a model, and generate an estimate like in Cimentada. Doing so would be advantageous because “it uses all the data. At some point, every rows gets to be the test set and training set, maximizing information. In fact, it uses almost ALL the data as the original data set as the training set is just N-1” (Cimentada, page 2, bullet points).
Regarding claim 9, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan further teaches
wherein the machine learning model comprises a neural network (Wan, page 13, 1st paragraph “Wasserstein generative adversarial networks with gradient penalty are a type of Generative Adversarial Networks (GANs), which are well-known to be highly capable of learning high-dimensional distributions from data samples. In general, conventional GANs are composed of two neural networks, i.e. the generator G and the discriminator (a.k.a. critic) D” where “in this work, we use the generator of well-trained WGAN-GP [Wasserstein generative adversarial networks with gradient penalty] models to generate synthetic samples” (Wan, page 13, last paragraph).)
Regarding claim 23, Wan teaches
A computing device, comprising processing circuitry (Wan, page 11, 1st paragraph, We further discuss the computational time cost (i.e. the actual running time obtained by using CPU-based PyTorch with a standard Linux computing cluster) and the training sample sizes (i.e. the number of training protein feature samples) for running FFPred-GAN to generate the optimal synthetic protein feature samples for individual GO terms.” Examiner notes that the CPU is the computing device and processing circuitry.)
appending the synthetic training dataset to the original training dataset to form a hybrid training dataset; and training the machine learning model using the hybrid training dataset (Wan, page 3, 2nd paragraph, “On the last step, FFPred-GAN uses the Classifier Two-Sample Tests (CTST) to select the optimal synthetic training protein feature samples, which are used to augment the original training samples. During the down-stream machine learning classifier training stage, the optimal synthetic samples are expected to derive better classifiers, leading to higher predictive accuracy” where “the SVM trained by the augmented training protein feature samples learning those decision boundaries that successfully separate the protein samples distributed on the right corner of the figure” (Wan, page 9, last paragraph). Examiner notes that the synthetic training dataset is the optimal synthetic training protein feature samples, the training dataset is the original training samples, and the hybrid training dataset is the augmented training protein feature samples. Examiner further notes that the augmenting the datasets is appending the synthetic training dataset to the training dataset to form a hybrid training dataset. Examiner also notes that the SVM is trained by the augmented training protein feature samples or the hybrid training dataset.).
Wan does not teach, but Cimentada does teach
selecting a feature ci of a training dataset as a target vector yi , the training dataset comprising a plurality of features (Cimentada, page 1, 2nd paragraph, “Let’s imagine a data set with 30 rows. We separate the 1st row to be the test data.” Examiner notes that the first row is the target vector and each row is a feature.);
selecting remaining features of the training dataset as a set of training vectors X\i, where X\i includes all features of the training dataset other than feature ci (Cimentada, page 1, 2nd paragraph, “We separate the 1st row to be the test data and the remaining 29 rows to be the training data.” Examiner notes that the training input vectors are the remaining 29 rows.);
training a prediction model f(yi|X\i) (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that fitting the model is training a prediction model.);
generating an estimate y'i of the target vector yi by applying the prediction model to the set of training vectors X\i (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that predicting the one observation left out is generating an estimate of the target vector.);
Wan and Cimentada are considered analogous to the claimed invention because they both use the Leave One Out method on generated data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan to select features, train a model, and generate an estimate like in Cimentada. Doing so would be advantageous because “it uses all the data. At some point, every rows gets to be the test set and training set, maximizing information. In fact, it uses almost ALL the data as the original data set as the training set is just N-1” (Cimentada, page 2, bullet points).
Wan in view of Cimentada teach the estimate y'i of the target vector yi, but does not teach inserting a synthetic feature corresponding to the estimate into a synthetic training dataset. Szeto does teach
memory coupled to the processing circuitry and including instructions that are executable by the processing circuitry to cause the computer device to perform operations comprising (Szeto, page 9, paragraph 0018, “The private data servers are computing devices having one or more processors that are configurable to execute software instructions stored in a non-transitory computer readable memory, where execution of the software instructions gives rise to a modeling engine on the private data server.” ):
and inserting a synthetic feature c'i corresponding to the estimate y'i of the target vector yi into a synthetic training dataset (Szeto, page 9, paragraph 0018, “The modeling engine further generates one or more private data distributions from the local private data training set where the private data distributions represent the nature of the local private data used to create the trained model. The modeling engine uses the private data distributions to generate a set of proxy data, which can be considered synthetic data or Monte Carlo data having the same general data distribution characteristics as the local private data, while also lacking the actual private or restricted features of the local, private data….The modeling engine then attempts to validate that the set of proxy data is a reasonable training set stand-in for the local, private data by creating a trained proxy model from the set of proxy data” where “From the private data distributions, the machine learning engine can identify or otherwise calculate one or more salient private data features that describe the nature of the private data distributions” (Szeto, page 10, paragraph 0019) and where “private data servers transmit salient features of aggregated private data to a non-private computing devices which in turn creates proxy data for integrating into a trained global model” (Szeto, page 10, paragraph 0026). Examiner notes that the proxy data is the synthetic training dataset. Examiner further notes that the estimate of the target vector is the salient private data features. By creating proxy data from the salient private data features, the synthetic features correspond to the estimate.).
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada to insert synthetic data into a dataset like in Szeto. Doing so “is considered advantageous because it provides for generating synthetic data capable of reproducing the knowledge gained from private data” (Szeto, page 18, paragraph 0078). Additionally, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented Wan in view of Cimentada on the computing device in Szeto. Thus, this would be applying a known technique (generating synthetic data) to a known device (memory coupled to the processing circuitry and including instructions) ready for improvement to yield predictable results (train machine learning models) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way).
Wan in view of Cimentada and Szeto do not explicitly disclose, but Hardy does disclose
and transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node (Hardy, page 3, 2nd column, 1st paragraph, “Workers perform iterations locally on their data and every E epochs (i.e., each worker passes E times the data in their GAN) they send the resulting parameters to the server” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn” (Hardy, page 3, 2nd column, first two bullet points) Examiner notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further notes that the indication from the master node is the server sending the generated data to the worker. Examiner additionally notes that the server is the master node and the workers transmit parameters or trained weights.)
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Regarding claim 37, claim 37 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 38, claim 38 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 39, claim 39 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 40, claim 40 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 41, claim 41 recites substantially similar limitations to claim 9, and is therefore rejected under the same analysis.
Regarding claim 42, Wan in view of Cimentada and Szeto teach the computing device of Claim 23. Wan in view of Cimentada and Szeto further teach appending the synthetic training dataset to the training dataset to form the hybrid training dataset and training the machine learning model as shown in claim 1. Wan in view of Cimentada and Szeto does not teach performing the appending the datasets and training the model in response to an indication from a master node in a federated learning system. However, Hardy does teach
wherein appending the synthetic training dataset to the original training dataset to form the hybrid training dataset and training the machine learning model are performed in response to an indication from a master node in a federated learning system (Hardy, page 3, 2nd column, first two bullet points, “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn.” Examiner notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further notes that the indication from the master node is the server sending the generated data to the worker.).
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Regarding claim 43, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada, Szeto, and Hardy further teaches
training a set of weights for a local machine learning model using a real training dataset (Hardy, page 3, 2nd column, 1st paragraph, “Workers perform iterations locally on their data and every E epochs (i.e., each worker passes E times the data in their GAN) they send the resulting parameters to the server” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn” (Hardy, page 3, 2nd column, first two bullet points) Examiner notes that the weights are the parameters, and the real training dataset is the batches of data.);
transmitting the set of weights of the local machine learning model to the master node (Hardy, page 3, 2nd column, 1st paragraph, “Workers perform iterations locally on their data and every E epochs (i.e., each worker passes E times the data in their GAN) they send the resulting parameters to the server” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn” (Hardy, page 3, 2nd column, first two bullet points) Examiner notes that the server is the master node and the workers transmit parameters or trained weights.);
and receiving, from the master node, a message with instructions to generate synthetic training data (Hardy, page 4, Figure 1b (see image below)
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Examiner notes that the server sends the generator to the workers which maps to receiving a message to generate synthetic training data.)
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Regarding claim 44, Wan in view of Cimentada, Szeto and Hardy teach the method of Claim 1. Wan in view of Cimentada, Szeto, and Hardy further teach
generating a quality metric measuring a quality of the synthetic training dataset (Hardy, page 5, 2nd column, 3rd paragraph, “Each worker n hosts a discriminator Dn and a training dataset Bn. It receives batches of generated imaged split into two parts Xn(d) and Xn(g). The generated images Xn(d) are used for training Dn to discriminate those generated images from real images. The learning is performed as a classical deep learning operation on a standlone server [1]. A worker n computes the gradient
∆
θ
n
of the error function Jdisc applied to the batch of generated images Xn(d) and a batch or real image Xn(r) taken from Bn. As indicated in Section II-1, this operation is iterated L times. The second batch Xn(g) of generated images is used to compute the error term F-n of generator G. Once computed, Fn is sent to the server for computation of gradients
∆
w
.” Examiner notes that the quality metric is the error term.);
Transmitting the quality metric to the master node(Hardy, page 5, 2nd column, 3rd paragraph, “Each worker n hosts a discriminator Dn and a training dataset Bn. It receives batches of generated imaged split into two parts Xn(d) and Xn(g). The generated images Xn(d) are used for training Dn to discriminate those generated images from real images. The learning is performed as a classical deep learning operation on a standlone server [1]. A worker n computes the gradient
∆
θ
n
of the error function Jdisc applied to the batch of generated images Xn(d) and a batch or real image Xn(r) taken from Bn. As indicated in Section II-1, this operation is iterated L times. The second batch Xn(g) of generated images is used to compute the error term F-n of generator G. Once computed, Fn is sent to the server for computation of gradients
∆
w
.” Examiner notes that the quality metric is the error term and the master node is the server.);
And receiving, from the master node, instructions to proceed with training the machine learning model using the synthetic training dataset (Hardy, page 5, 2nd column, 3rd paragraph, “Each worker n hosts a discriminator Dn and a training dataset Bn. It receives batches of generated imaged split into two parts Xn(d) and Xn(g). The generated images Xn(d) are used for training Dn to discriminate those generated images from real images. The learning is performed as a classical deep learning operation on a standlone server [1]. A worker n computes the gradient
∆
θ
n
of the error function Jdisc applied to the batch of generated images Xn(d) and a batch or real image Xn(r) taken from Bn. As indicated in Section II-1, this operation is iterated L times. The second batch Xn(g) of generated images is used to compute the error term F-n of generator G. Once computed, Fn is sent to the server for computation of gradients
∆
w
.” Examiner notes that the quality metric is the error term and the master node is the server. Examiner further notes that by sending the error term to the server, the master node is receiving instructions to proceed with training since the error term is used further for the computation of gradients.);
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan in view of Cimentada in further view of Szeto, Hardy, and He et al. (US 2020/0386811 A1) (hereafter referred to as He).
Regarding claim 12, Wan in view of Cimentada, Szeto, and Hardy teach the method of Claim 1. Wan further teaches
providing a preliminary training dataset (Wan, page 3, 2nd paragraph, “To begin with, FFPred-GAN adopts the widely-used FFPred feature extractor to derive protein biophysical information based on the raw amino acid sequences.” Examiner notes that the raw amino acid sequences are the preliminary dataset.);
splitting the preliminary training dataset into the original training dataset and a verification dataset before generating the synthetic training dataset (Wan, page 14, last paragraph, “The protein set for each GO term was further split into the training and testing protein sets with a proportion of 7:3. The total 258 dimensions of protein sequence-derived biophysical features…are used to describe the proteins.” Examiner notes that the protein set is the preliminary training dataset.);
performing feature reduction on the preliminary training dataset before splitting the preliminary training dataset into the original training dataset and the verification dataset (Wan, page 3, 2nd paragraph, “To begin with, FFPred-GAN adopts the widely-used FFPred feature extractor to derive protein biophysical information based on the raw amino acid sequences. For each protein sequence, 258 dimensional features are generated to describe 13 groups of protein biophysical information, such as secondary structure, amino acid composition and presence of motifs” where “we train two FFPred-GAN models for each GO term by using two different sets of protein samples with different class labels” (Wan, page 3, last paragraph) and “the protein set for each GO term was further split into the training and testing protein sets with a proportion of 7:3” (Wan, page 14, last paragraph). Examiner notes that the raw amino acid sequences are the preliminary dataset and the feature extractor is feature reduction. Examiner further notes that the feature extraction occurs and produces protein samples or protein sets. After the protein samples are extracted, the samples are split into training and verification datasets.);
Wan in view of Cimentada does not teach, but Szeto does teach
verifying the machine learning model using the verification dataset (Szeto, page 21, paragraph 0098, “In other embodiments, proxy data 422 can be partitioned into training and validation data sets, which can then be used for cross-fold validation….the validation proxy data would be provided to the trained proxy model for validation” and “The model instructions can be considered as one or more command that instruct the modeling engine to use at least some of the local private data in order to create a trained actual model according to an implementation of a machine learning algorithm (e.g., support vector machine, neural network)” (Szeto, page 9, paragraph 0018). Examiner notes that the neural network is the trained proxy model and the verification dataset is the validation data set.);
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada to verify the neural network using the verification dataset like in Szeto. Doing so “ensure[s] a proper analysis” (Szeto, page 24, paragraph 0121).
Wan in view of Cimentada, Szeto, and Hardy does not teach, but He does teach
and sorting the preliminary training dataset in descending order according to an importance of the features (He, page 5, paragraph 0009, “fault feature dimensionality reduction preprocessing, wherein an importance score of all features is calculated by ET algorithm, then and the features are sorted in descending order according a value of the importance score, and a new feature set is obtained by removing the features with a low importance score with a determined proportion.” Examiner notes that the features are the preliminary training dataset.).
Wan in view of Cimentada, Szeto, and Hardy and He are analogous to the claimed invention because they teach machine learning models that perform feature reduction. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to sort the training dataset in descending order like in Szeto. Doing so “avoids a long training time resulting from overly high dimensionality of the feature data” (He, page 10, paragraph 0069).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan in view of Cimentada, Szeto, and Hardy, and Weng (“From GAN to WGAN”) (hereafter referred to as Weng).
Regarding claim 15, Wan in view of Cimentada, Szeto, and Hardy teach the method of Claim 1. Wan in view of Cimentada, Szeto, and Hardy does not teach, but Weng does teach
computing a Kullback-Leibler divergence between the original training dataset and the synthetic training dataset to determine a quality of the original training dataset (Weng, page 2, 2nd paragraph, “Before we start examining GANs closely, let us first review two metrics for quantifying the similarity between two probability distributions. (1) KL (Kullback-Leibler) divergence measures how one probability distribution p diverges from a second expected probability distribution q.” Examiner notes that p and q are the training dataset and synthetic training dataset and the quality of the training dataset is the similarity between p and q.).
Wan in view of Cimentada, Szeto, and Hardy and Weng are analogous to the claimed invention because they generate synthetic data to train prediction models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have implemented the prediction model in Wan in view of Cimentada and Szeto to use a Kullback-Leibler divergence like in Szeto. Doing so “quantif[ies] the similarity between two probability distributions” (Weng, page 2, 3rd paragraph).
Claim(s) 28 and 30-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan in view of Cimentada, and in further view of Szeto, Hardy and Guillame-Bert et al. (US 2019/0251468 A1) (hereafter referred to as Guillame-Bert).
Regarding claim 28, Wan teaches
append the synthetic training dataset to the original training dataset to form a hybrid training dataset; and train a machine learning model using the hybrid training dataset (Wan, page 3, 2nd paragraph, “On the last step, FFPred-GAN uses the Classifier Two-Sample Tests (CTST) to select the optimal synthetic training protein feature samples, which are used to augment the original training samples. During the down-stream machine learning classifier training stage, the optimal synthetic samples are expected to derive better classifiers, leading to higher predictive accuracy” where “the SVM trained by the augmented training protein feature samples learning those decision boundaries that successfully separate the protein samples distributed on the right corner of the figure” (Wan, page 9, last paragraph). Examiner notes that the synthetic training dataset is the optimal synthetic training protein feature samples, the training dataset is the original training samples, and the hybrid training dataset is the augmented training protein feature samples. Examiner further notes that the augmenting the datasets is appending the synthetic training dataset to the training dataset to form a hybrid training dataset. Examiner also notes that the SVM is trained by the augmented training protein feature samples or the hybrid training dataset.).
Wan does not teach, but Cimentada does teach
select a feature ci of the original training dataset as a target vector yi (Cimentada, page 1, 2nd paragraph, “Let’s imagine a data set with 30 rows. We separate the 1st row to be the test data.” Examiner notes that the first row is the target vector.);
select remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the original training dataset other than a feature corresponding to the selected feature ci (Cimentada, page 1, 2nd paragraph, “We separate the 1st row to be the test data and the remaining 29 rows to be the training data.” Examiner notes that the training input vectors are the remaining 29 rows.);
train a prediction model f(yi|X\i) (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that fitting the model is training a prediction model.);
generate an estimate y'i of the target vector yi by applying the prediction model to the set of training vectors X\i (Cimentada, page 1, 2nd paragraph, “We fit the model on the training data and then predict the one observation we left out.” Examiner notes that predicting the one observation left out is generating an estimate of the target vector.);
Wan and Cimentada are considered analogous to the claimed invention because they both use the Leave One Out method on generated data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan to select features, train a model, and generate an estimate like in Cimentada. Doing so would be advantageous because “it uses all the data. At some point, every rows gets to be the test set and training set, maximizing information. In fact, it uses almost ALL the data as the original data set as the training set is just N-1” (Cimentada, page 2, bullet points).
Wan in view of Cimentada teach the estimate y'i of the target vector yi, but does not teach inserting a synthetic feature corresponding to the estimate into a synthetic training dataset. Szeto does teach
insert a synthetic feature c'i corresponding to the estimate y'i …into a synthetic training dataset (Szeto, page 9, paragraph 0018, “The modeling engine further generates one or more private data distributions from the local private data training set where the private data distributions represent the nature of the local private data used to create the trained model. The modeling engine uses the private data distributions to generate a set of proxy data, which can be considered synthetic data or Monte Carlo data having the same general data distribution characteristics as the local private data, while also lacking the actual private or restricted features of the local, private data….The modeling engine then attempts to validate that the set of proxy data is a reasonable training set stand-in for the local, private data by creating a trained proxy model from the set of proxy data” where “From the private data distributions, the machine learning engine can identify or otherwise calculate one or more salient private data features that describe the nature of the private data distributions” (Szeto, page 10, paragraph 0019) and where “private data servers transmit salient features of aggregated private data to a non-private computing devices which in turn creates proxy data for integrating into a trained global model” (Szeto, page 10, paragraph 0026). Examiner notes that the proxy data is the synthetic training dataset. Examiner further notes that the estimate is the salient private data features. By creating proxy data from the salient private data features, the synthetic features correspond to the estimate.)
Wan in view of Cimentada and Szeto are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada to insert synthetic data into a dataset like in Szeto. Doing so “is considered advantageous because it provides for generating synthetic data capable of reproducing the knowledge gained from private data” (Szeto, page 18, paragraph 0078).
Wan in view of Cimentada and Szeto does not disclose, but Hardy does teach
transmitting, …, a message to at least one of the workers instructing the at least one worker to generate synthetic training data by instructing the at least one of the workers to: (Hardy, page 4, Figure 1b (see image below)
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Examiner notes that the server sends the generator to the workers which maps to transmitting a message to the workers to generate synthetic training data.)
transmit weights of the machine learning model, trained using the hybrid dataset, to the master node (Hardy, page 3, 2nd column, 1st paragraph, “Workers perform iterations locally on their data and every E epochs (i.e., each worker passes E times the data in their GAN) they send the resulting parameters to the server” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
. The way in which the two distinct batches are selected is discussed in Section IV-B1. Each worker n performs L learning iterations on its discriminator Dn (see Section II-1) using
X
n
(
d
)
and
X
n
(
r
)
, where
X
n
(
r
)
is a batch of real data extracted locally from Bn” (Hardy, page 3, 2nd column, first two bullet points) Examiner notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further notes that the indication from the master node is the server sending the generated data to the worker. Examiner additionally notes that the server is the master node and the workers transmit parameters or trained weights.)
and receiving, …, model parameters of a machine learning model from the at least one worker that were generated using the synthetic training data (Hardy, page 4, Figure 1b (see image below)
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Examiner notes that since the discriminator is sent to the server and the discriminator has updated parameters, the model parameters from workers that were generated using the synthetic data were received by the server.)
wherein the model parameters received from the worker comprise trained neural network weights (Hardy, page 4, Figure 1b (see image below)
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and “A GAN is a machine learning model, and more specifically a certain type of deep neural networks” (Hardy, page 1, 1st column, 3rd paragraph). Examiner notes that the updated parameters from the discriminator of the neural network are the trained neural network weights.).
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Wan, Cimentada, Szeto, and Hardy do not explicitly disclose a message bus. Guillame-Bert, however does disclose
A method of operating a master node in a federated learning system including a plurality of workers that communicate with the master node via a message bus (Guillame-Bert, page 9, paragraph 0044, “DRF [Distributed Random Forest algorithm] computation can be distributed among computing machines called “workers”, and coordinated by a “manager”. The manager and the workers can communicate through a network.” Examiner notes that the network is the message bus.)
the message bus(Guillame-Bert, page 9, paragraph 0044, “DRF [Distributed Random Forest algorithm] computation can be distributed among computing machines called “workers”, and coordinated by a “manager”. The manager and the workers can communicate through a network.” Examiner notes that the network is the message bus.)
Wan, Cimentada, Szeto, Hardy and Guillame-Bert are analogous to the claimed invention because they use federated learning models. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented Wan, Cimentada, Szeto and Hardy on the federated learning system that includes the message bus in Guillame-Bert. Thus, this would be applying a known technique (federated learning) to a known device (message bus) ready for improvement to yield predictable results (communicate with master node) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way).
Regarding claim 30, Wan in view of Cimentada, Szeto, Hardy, and Guillame-Bert teach the method of Claim 28. Wan in view of Cimentada, Szeto, Hardy, and Guillame-Bert further teach
receiving from the at least one worker a set of preliminary neural network weights that were trained without using the synthetic training data (Hardy, page 5, 2nd column, 3rd paragraph, “Each worker n hosts a discriminator Dn and a training dataset Bn” where “Each discriminator n solely uses Bn- to train its parameters θn” (Hardy, page 5, 2nd column, 4th paragraph) and (Hardy, page 4, Figure 1b (see image below)
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Examiner notes that the local training dataset Bn is used to train preliminary network weights or the parameters without using synthetic data since Bn is local to the discriminator. Examiner further notes that since the discriminator is sent to the server, the preliminary neural network weights or parameters are also received in the master node.);
and evaluating the set of preliminary neural network weights (Hardy, page 5, 2nd column, 3rd paragraph, “Each worker n hosts a discriminator Dn and a training dataset Bn” where “Each discriminator n solely uses Bn- to train its parameters θn” (Hardy, page 5, 2nd column, 4th paragraph) and “workers only have to handle their discriminator parameters θn and to compute error feedbacks after L local iterations” (Hardy, page 5, 2nd column, 5th paragraph). Examiner notes that computing error feedbacks based after local iterations is evaluating the set of preliminary neural network weights.),
wherein transmitting the message to the at least one worker instructing the at least one worker to generate the synthetic training data is performed in response to evaluating the set of preliminary neural network weights (Hardy, page 4, 1st column, 2nd to last paragraph, “the server generates new images to train all discriminators and updates w using error feedbacks” and where “workers only have to handle their discriminator parameters θn and to compute error feedbacks after L local iterations” (Hardy, page 5, 2nd column, 5th paragraph). Examiner notes that the server or master node generates synthetic data using error feedbacks sent from the worker where the computing the error feedback is evaluating the set of preliminary neural network weights.).
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Regarding claim 31, Wan in view of Cimentada, Szeto, Hardy, and Guillame-Bert teach the method of Claim 28. Wan in view of Cimentada, Szeto, Hardy, and Guillame-Bert further teach
after instructing the at least one worker to generate the synthetic training data, receiving a quality metric from the at least one worker, wherein the quality metric measures a quality of a synthetic training dataset (Hardy, page 4, 2nd column, 2nd paragraph, “Every global iteration, the server receives the error feedback Fn from every worker n, corresponding to the error made by G on
X
n
(
g
)
” where “The server generates a set K of k batches K = {X(1),…, X(k)}, with k ≤ N. Each X(i) is composed of b data generated by G. The server then selects, for each worker n, two distinct batches, say X(i) and X(j), which are sent to worker n and locally renamed as
X
n
(
g
)
and
X
n
(
d
)
” (Hardy, page 3, 2nd column, first bullet point) Examiner notes that G is the generator on the master node or server,
X
n
(
g
)
is the generated or synthetic training dataset, and the error feedback is the quality metric. Since the error feedback is computed on the synthetic dataset from the server, receiving the quality metric happens after instructing the worker to generate synthetic training data.);
and instructing the worker to proceed with training a machine learning model using the synthetic training dataset in response to the quality metric (Hardy , page 5, Algorithm 1 (see image below)
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Examiner notes that between lines 27 and 40, the server iteratively sends generated data (line 34) and gets feedback about the generated data (line 36). After computing gradients and updating the gradients (lines 37-39), the server proceeds to start the for loop over in which the server once again sends generated data to the worker. Examiner further notes that between lines 2 and 13, the worker also iteratively receives the generated data from the server (line 5) and sends feedback to the server (line 10). By iteratively performing the steps of sending data and receiving feedback, the server is instructing the worker to proceed with training.).
Wan in view of Cimentada, Szeto and Hardy are analogous to the claimed invention because they generate synthetic data to train machine learning models. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wan in view of Cimentada and Szeto to use a federated learning system to generate synthetic data to train a model like in Hardy. Doing so “address[es] the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers” (Hardy, page 1, abstract).
Response to Arguments
Examiner notes that the previous 112(b) rejections have been overcome.
On page 10-11, Applicant argues:
Claim 23 recites similar features. A combination of Wan, Cimentada, and Szeto fails to teach or suggest each and every feature of these claims.
As previously argued, the combination of art fails to teach or suggest "appending the synthetic training dataset to the original training dataset to form a hybrid training dataset and training the machine learning model using the hybrid training dataset." In the response to arguments on pages 60-61 of the Office Action, the Office asserts, "that under broadest reasonable interpretation, "appending the synthetic training dataset to the original training dataset" includes adding the synthetic training dataset as a supplement to the original training dataset." One of ordinary skill in the art would appreciate, as discussed in Wan, that the claimed augmenting is not appending as recited in the claims. Specifically, Wan is not appending a synthetic dataset but is merely augmenting a subset of synthetic data (e.g., the optimal feature samples) because Wan "select[s] the optimal synthetic training protein feature samples, which are used to augment the original training samples." Therefore, Wan is not appending the synthetic training protein feature samples, but is selecting a subset of the synthetic training protein feature samples, then using that subset to perform some augmentation of the original In re: Selim ICKIN et al. training samples. Further, there is no discussion in Wan that the augmentation is an appending process (e.g., adding data to another data set). Instead, unless otherwise indicated Wan refers to augmenting the original training samples, thus modifying the original training samples themselves, which is not appending one dataset to another. Therefore, augmenting the original training samples fails to teach or suggest forming a hybrid training dataset by appending the synthetic training dataset to the original training data set.
Moreover, the combination of art fails to teach or suggest "transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node." In the rejection of claim 28, the Office asserts that "Hardy, page 3, 2nd column, 1st paragraph, discusses transmit[ing] weights of the machine learning model to the master node. Hardy does not teach or suggest "transmitting weights of the machine learning model, trained using the hybrid dataset," as recited in claim 1. As discussed above, the hybrid dataset is created by appending the synthetic training dataset to the original training dataset to form a hybrid training dataset. However, Hardy is silent with respect to the use of synthetic training data. As such, Hardy cannot reasonably be construed to teach or suggest "transmitting weights of the machine learning model, trained using the hybrid dataset," as recited in claim 1.
For at least these reasons, independent claim 1 is allowable. While different in scope, independent claim 23 recites similar features and is also allowable. The dependent claims, which add further recitations to the patent eligible subject matter, are also patent-eligible under 35 U.S.C. § 103. Accordingly, Applicant respectfully requests the withdrawal of the § 103 rejections.
Regarding the applicant’s argument that the prior art of record does not teach the limitation of “appending the synthetic training dataset to the original training dataset to form a hybrid training dataset and training the machine learning model using the hybrid training dataset”, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that Wan teaches this. Examiner notes that under broadest reasonable interpretation, “appending the synthetic training dataset to the original training dataset” includes adding the synthetic training dataset as a supplement to the original training dataset. In this case, by augmenting the original training dataset with the synthetic training dataset, the synthetic training dataset has been appended to the original training dataset to form a hybrid training dataset.
Regarding the Applicant’s argument that the prior art fails to teach “transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node”, Examiner respectfully disagrees. Specifically, Examiner notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further notes that under the broadest reasonable interpretation of the claim, the weights do not need be trained using the hybrid dataset. Rather, the machine learning model has been interpreted to be trained using the hybrid dataset.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On page 12, Applicant argues:
A combination of Wan, Cimentada, Szeto, Hardy, and Guillame-Bert fails to teach or suggest each and every feature of claim 28. For example, the combination fails to teach or suggest instructing the workers to "append the synthetic training dataset to the original training dataset to form a hybrid training dataset." As explained above, Wan, Cimentada, and Szeto, fail to teach or suggest these features and Hardy and Guillame-Bert are not cited as teaching these features.
Hardy fails to teach or suggest, "transmitting, via the message bus, a message to at least one of the workers instructing the at least one worker to generate synthetic training data" which includes "transmit weights of the machine learning model to the master node" where the transmitted weights are from the training a machine learning model using the hybrid training dataset. The Office asserts that page 3, col. 2, and page 4, Figure lb of Hardy teaches these features. However, Hardy is silent with respect to the use of synthetic training data, much less "transmitting ... a message to at least one of the workers instructing the at least one worker to generate synthetic training data," as recited in claim 28. As such, Hardy cannot reasonably be construed to teach or suggest the above-noted features of claim 28.
Furthermore, it is established law that one "cannot use hindsight reconstruction to pick and choose among isolated disclosures in the prior art to deprecate the claimed invention." Ecolochem, Inc. v. Southern Calif Edison Co., 227 F.3d 1361 (Fed. Cir. 2000), (citing In re Fine, 837 F.2d 1071, 1075, 5 USPQ2d 1780, 1783 (Fed. Cir. 1988)). In this rejection, the Office has clearly selected the elements of claim 28 alleged these elements to be present in each of Cimentada, Szeto, Hardy, and Guillame-Bert, and alleged that a person of skill in the art would have incorporated these elements into Wan. Applicant traverses this rejection as this is a clear example of "blueprinting," which renders the rejection improper as being based upon impermissible hindsight.
For at least these reasons, independent claim 28 is allowable. The dependent claims, which add further recitations to the patent eligible subject matter, are also patent-eligible under 35 U.S.C. § 103. Accordingly, Applicant respectfully requests the withdrawal of the § 103 rejections.
Regarding the applicant’s argument that the prior art of record does not teach the limitation of “append the synthetic training dataset to the original training dataset to form a hybrid training dataset”, the Examiner respectfully disagrees. Specifically, Examiner respectfully notes that Wan teaches this. Examiner notes that under broadest reasonable interpretation, “appending the synthetic training dataset to the original training dataset” includes adding the synthetic training dataset as a supplement to the original training dataset. In this case, by augmenting the original training dataset with the synthetic training dataset, the synthetic training dataset has been appended to the original training dataset to form a hybrid training dataset.
Regarding the Applicant’s argument that the prior art does not teach “transmitting, via the message bus, a message to at least one of the workers instructing the at least one worker to generate synthetic data”, Examiner respectfully disagrees. Specifically, Examiner notes that Hardy teaches “transmitting…a message to at least one of the workers instruction the at least one worker to generate synthetic data” (Hardy, page 4, Figure 1b). Examiner further notes that Hardy performs iterations using both real and generated data (Hardy, page 3, 2nd column, 1st paragraph; Hardy, page 3, 2nd column, first two bullet points) which is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model (as taught by claim 1, 23, and 28). Examiner further notes that the indication from the master node is the server sending the generated data to the worker. Examiner additionally notes that the server is the master node and the workers transmit parameters or trained weights.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On pages 12-13, Applicant argues:
Dependent claim 42 recites, "wherein appending the synthetic training dataset to the original training dataset to form the hybrid training dataset and training the machine learning model are performed in response to an indication from a master node in a federated learning system." Applicant agrees that the combination of Wan, Cimentada, and Szeto fails to disclose the features of claims 10 and 42 (see, page 42 of the Office Action), however, Hardy does not cure the deficiencies of Wan, Cimentada, and Szeto. For example, the combination fails to teach or suggest performing "appending ... in response to an indication from a master node in a federated learning system." With respect to the rejection of claim 1, the Office asserts that the appending step is taught by the augmenting of data in Wan (see, page 30 of the Office Action), which is not performed in response to a server sending generated data to a worker. Thus, per the logic set forth by the Office, the appending is performed in response to "the server sending the generated data to the worker" and not "in response to an indication from a master node in a federated learning system," as recited in claim 42. As such, the combination of Wan, Cimentada, Szeto, and Hardy cannot reasonably be construed to teach or suggest the above-noted features of claim 42. Accordingly, Applicant respectfully requests the withdrawal of the§ 103 rejection.
Regarding the Applicant’s argument that the prior art does not teach claims 10 and 42, Examiner respectfully disagrees. Specifically, Examiner notes that claim 10 has been canceled. Examiner respectfully further notes that Hardy also teaches appending a synthetic training dataset to the original training dataset. Examiner additionally notes that performing iterations using both real and generated data is appending the synthetic training dataset to the training dataset to form a hybrid dataset and training the model. Examiner further respectfully notes that the indication from the master node is the server sending the generated data to the worker. After sending the generated data to the worker, the worker appends the generated data to the real data by training on both sets.
On page 11, Applicant argues:
New Claims
Claims 43 and 44 are added by this Amendment and are believed to be allowable for the following reasons. Claims 43 and 44 depend from claim 1, and are distinguishable from the applied art for at least the same reasons as the base claim. Moreover, Claims 43 and 44 recite additional combinations of features that are not taught by the applied art.
Dependent Claims 2, 4-5, 7, 9-12, 15, 30-31, and 37-42 are Patentable
The dependent claims are patentable at least per the patentability of the independent claims from which they depend. Applicant traverses the rejections of the remaining dependent claims. However, as each of these claims depends from a base claim that is believed to be in condition for allowance, Applicant does not believe that it is necessary to argue the allowability of each of the remaining dependent claim individually. Applicant does not necessarily concur with the interpretation of these claims or with the bases for rejection set forth in the Office Action. Applicant therefore reserves the right to address the patentability of these claims individually as necessary in the future.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On pages 14-15, Applicant argues:
As previously argued, independent claim 1 is patent-eligible under 35 U.S.C. § 101, notwithstanding, Applicant notes the updated guidance and U.S. Patent and Trademark Office's (USPTO's) Appeals Review Panel (ARP) decision that provide additional support for the eligibility of claim 1. Specifically, on August 4, 2025, the Patent Office issued a memorandum to the Technology Centers 2100, 2600, and 3600 entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter the "Eligibility memo"). The Eligibility memo provides "guidance on the following topics that arise when examiners assess Step 2A of the USPTO's subject matter eligibility analysis" and the Eligibility memo "includes a discussion In re: Selim ICKIN et al. of when a subject matter eligibility rejection should be made." In addition to the Eligibility memo, on December 5, 2025, the Patent Office issued a memorandum to the Patent Examining Corps entitled "advance notice of change to the MPEP in light of Ex Parte Desjardins" that discussed the Ex Parte Desjardins decision (hereinafter the "Desjardins memo"). As summarized in the Desjardins memo, the Ex Parte Desjardins decision analyzed eligibility in terms of whether the claims were directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field under longstanding Federal Circuit precedent in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). See also MPEP §§ 2106.04(d)(l) and 2106.05(a). The combination of the Eligibility memo and the Desjardins memo has clarified how claims should be evaluated under 35 U.S.C. 101.
Regarding the Applicant’s argument that the memos have changed the process of examining, Examiner respectfully disagrees. Specifically, Examiner notes that the memos have not changed the process of examining the claims under 101.
On pages 15-16, Applicant argues:
Following the logic outlined in the Eligibility memo, independent claim 1 is not directed to an abstract idea and cannot practically be performed in the human mind. Instead, the claim recites a specific method of generating a synthetic training dataset for training a machine learning model using an original training dataset including a plurality of features. For example, claim 1 recites "selecting a feature Ci of the original training dataset as a target vector Yi," "selecting remaining features of the original training dataset as a set of training input vectors X\i," "training a prediction model f(yilX\i)," "generating an estimate y'i of the target vector Yi by applying the prediction model to the set of training vectors X\i," "inserting a synthetic feature c'I corresponding to the estimate y'i of the target vector Yi into the synthetic training dataset," "appending the synthetic training dataset to the original training dataset to form a hybrid training dataset," as well as "training the machine learning model using the hybrid training dataset." One of ordinary skill in the art would understand that such a specific combination steps are not merely mental processes that can be practically performed in the human mind. As an example, one of ordinary skill in the art would appreciate that machine learning requires processing data at a scale that is not reasonably possible by the human mind. Thus, one of ordinary skill would understand that the steps of independent claim 1 are not that in which the human mind is not equipped to perform and thus is not directed to an abstract idea.
Regarding the Applicant’s argument that the claims are not directed towards a mental process, the Examiner respectfully disagrees. Specifically, generating a synthetic training dataset encompasses a human mentally creating a dataset and is thus an evaluation. Selecting a feature ci of the original training dataset as a target vector yi encompasses a human mentally selecting a feature as a target vector and is thus a judgement. Selecting remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the original training dataset other than a feature corresponding to the selected feature ci encompasses a human mentally selecting features as vectors and is thus a judgment. Generating an estimate y'i of the target vector yi encompasses a human mentally creating an estimate and is thus an evaluation. Inserting a synthetic feature c'i corresponding to the estimate y'i of the target vector yi into the synthetic training dataset encompasses a human mentally inserting a feature into a dataset, and appending the synthetic training dataset to the original training dataset to form a hybrid training dataset encompasses a human mentally combining datasets and is thus an evaluation. Training a prediction model f(yi | X\i) and training the machine learning model using the hybrid training dataset do not integrate the abstract idea into a practical application because they recites insignificant extra-solution activity of training a model using a dataset (see MPEP 2106.05(g)). They further are the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”).
On pages 16-17, Applicant argues:
Even if, arguendo, the claim were to be considered directed to an abstract idea under Step 2A, Prong One, independent claim 1 satisfies Step 2A, Prong Two because the alleged abstract idea is integrated into a practical application. The Eligibility memo notes that, in Step 2A Prong Two, the Office should consider "the claim as a whole" such that "the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception. Instead, the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application." (See, pages 2-3 of the Eligibility memo). The Eligibility memo further notes how the Office "can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception." Lastly, in the Eligibility memo, the "examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement" noting that "The claim itself does not need to explicitly recite the improvement described in the specification."
Paragraphs [0059]-[0061] of Applicant's Specification identifies various challenges and issues that exist when generating synthetic data for use in machine learning and quality of experience modeling. As noted in paragraph [0062] of Applicant's Specification, "These and other challenges are addressed by embodiments described herein, which provide a non-GAN based approach to the generation of synthetic datasets," including the steps provided by independent claim 1. Paragraphs [0068]-[0078] discuss how the claimed invention provides advantages and technical improvements, for example, increasing a training accuracy of workers , as discussed in paragraph [0072]. Therefore, independent claim 1 reflects an improvement to the functioning of a computer and/or to another technology or technical field, integrating any alleged judicial exception into a practical application of that exception.
Regarding the Applicant’s argument that the claims are integrated into a practical application, the Examiner respectfully disagrees. Specifically, the claims do not reflect the improvement to the technology as stated in paragraphs 0059-0062 and therefore cannot integrate the claims into a practical application (MPEP 2106.04 (d) (1)). Regarding the Applicant’s argument that these elements provide an improvement, Examiner respectfully disagrees. Specifically, Examiner notes that the Applicant provides a bare assertion of an improvement in paragraph 0072 without the detail necessary to be apparent to one of ordinary skill in the art and, thus, cannot provide an improvement (MPEP 2106.04(d)(1)).
Furthermore, Examiner respectfully notes that the additional elements do not integrate the claims into a practical application because for training a machine learning model using an original training dataset including a plurality of features, recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)), training a prediction model f(yi | X\i) recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)), by applying the prediction model to the set of training vectors X\i; amounts to mere “apply it on a computer” (see MPEP 2106.05(f)), training the machine learning model using the hybrid training dataset recites insignificant extra-solution activity of training a model using a dataset (see MPEP 2106.05(g)), and transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).).
On page 17, Applicant argues:
Moreover, even if, arguendo, the additional elements do not integrate the exception into a practical application, then the Office must evaluate whether the claim provides an inventive concept, under Step 2B analysis. As noted in the Eligibility memo, "the examiner should consider whether the technological limitations are being used as a tool to improve the recited judicial exception ... or whether the claim as a whole provides an improvement to technology or a technical field. Claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself." Independent claim 1 provides a non-conventional arrangement of steps for generating "a synthetic training dataset for training a machine learning model." This is not a routine or conventional use of a generic computer, but rather a specific logic flow that increases a training accuracy of workers. Therefore, the combination of steps for independent claim 1 satisfies the "significantly more" requirement.
Regarding the Applicant’s argument that the claims provide significantly more, Examiner respectfully disagrees. Specifically, Examiner respectfully notes that for training a machine learning model using an original training dataset including a plurality of features specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)), training a prediction model f(yi | X\i) is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”), by applying the prediction model to the set of training vectors X\i uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)), training the machine learning model using the hybrid training dataset is the well understood, routine, and conventional activity of training a model (Yan et al., US 2019/0367019 A1, page 17, paragraph 0049, “As well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”), and transmitting weights of the machine learning model, trained using the hybrid dataset, to a master node is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
On pages 17-18, Applicant argues:
For at least these reasons, independent claim 1 is directed to patent-eligible subject matter. While different in scope, independent claims 23 and 28 recite similar features and are also directed to patent eligible subject matter. The dependent claims, which add further recitations to the patent eligible subject matter, are also patent-eligible under 35 U.S.C. § 101. Accordingly, Applicant respectfully requests the withdrawal of the§ 101 rejection.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (“Poisoning Attack in Federated Learning using Generative Adversarial Nets”) also discusses using a GAN in a federated learning environment.
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/K.R.L./Examiner, Art Unit 2148
/PAUL M KNIGHT/Examiner, Art Unit 2148
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