DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 12 January 2024.
Claims 1-24 are pending. Claims 1 and 13 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5 February 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The examiner accepts the drawings filed 12 January 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-9, 11, and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 7, the claim recites "the classification algorithm" in line 1. There is insufficient antecedent basis for this limitation in the claim.
The examiner notes that independent claim 1, on which claim 7 depends, and dependent claim 8, which depends upon claim 7, recite “a classification model algorithm (claim 1, lines 7-8)” and “the classification model algorithm (claim 8, line 1).” For the purpose of examination, the examiner will treat claim 7 as though it recites “the classification model algorithm.”
Claims 8-9 fail to cure the deficiency of claim 7. Claims 8-9 are rejected under similar rationale.
With respect to claims 11 and 23, the term “optimal classification accuracy (claim 11, line 3; claim 23, line 3) is a relative term which renders the claim indefinite. The term “optimal classification accuracy” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-12 are directed toward a system (machine). Claims 13-24 are directed toward a method (process). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites, in part:
determine performance constraints associated with deployment and execution of a machine learning classification model (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine performance constraints associated with a machine learning classification model)
identify a plurality of candidate classification model pipelines, each pipeline comprising a different combination of data preprocessing techniques, a classification model algorithm, and hyperparameter tuning values (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify a plurality of classification model pipelines comprising different combinations of preprocessing techniques, classification model algorithms, and hyperparameter tuning values)
for each candidate classification model pipeline:
processing the training dataset using the data preprocessing techniques (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique)
comparing the performance characteristics to the performance constraints to identify whether the trained model meets the performance constraints (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement by comparing the performance characteristics to the performance constraints to perform an evaluation to identify whether the trained model meets the performance constraints)
identify one of the candidate classification model pipelines that meets the performance constraints (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify one of the candidate classification model pipelines that meets the performance constraints)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
a system for constraint-based optimization of machine learning classification models, the system comprising a server computing device having a memory that stores computer-executable instructions and a processor that executes the computer-executable instructions
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
training the classification model algorithm on the training dataset
turning the trained classification model algorithm using the hyperparameter tuning values
executing the trained classification model using a testing dataset as input to determine performance characteristics for the trained model
build a production classification model based upon the identified candidate model pipeline
The training, tuning, testing, and building a model elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim recites the additional elements:
deploy the production classification model in a production computing environment
These limitations amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
a system for constraint-based optimization of machine learning classification models, the system comprising a server computing device having a memory that stores computer-executable instructions and a processor that executes the computer-executable instructions
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
training the classification model algorithm on the training dataset
turning the trained classification model algorithm using the hyperparameter tuning values
executing the trained classification model using a testing dataset as input to determine performance characteristics for the trained model
build a production classification model based upon the identified candidate model pipeline
The training, tuning, testing, and building a model elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim recites the additional elements:
deploy the production classification model in a production computing environment
These limitations amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the performance constraints comprise a maximum response time, a maximum CPU usage, a maximum memory usage, and a maximum platform execution cost (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine performance constraints, wherein the performance constraints comprise a maximum response time, a maximum CPU usage, a maximum memory usage, and a maximum platform execution cost, associated with a machine learning classification model)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 3:
With respect to claim 3, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the data preprocessing algorithm comprises an imputation step, a feature scaling step, and an encoding step (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique, wherein the preprocessing comprises an imputation step, a feature scaling step, and an encoding step)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 4:
With respect to claim 4, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the data imputation step comprises mean imputation or median imputation (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique, wherein the preprocessing comprises an imputation step comprising a mean imputation or median imputation, a feature scaling step, and an encoding step)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 5:
With respect to claim 5, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the feature scaling step comprises standardization or normalization (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique, wherein the preprocessing comprises an imputation step, a feature scaling step comprising a standardization or normalization, and an encoding step)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 6:
With respect to claim 6, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the encoding step comprises one-hot encoding or dummy encoding (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique, wherein the preprocessing comprises an imputation step, a feature scaling step, and an encoding step comprising one-hot encoding or dummy encoding)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 7:
With respect to claim 7, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the classification algorithm comprises a k-nearest neighbor (KNN) algorithm or a support vector machine (SVM) algorithm (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify a plurality of classification model pipelines comprising different combinations of preprocessing techniques, classification model algorithms comprising a k-nearest neighbor or support vector machine algorithm, and hyperparameter tuning values)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the classification model algorithm is a KNN algorithm, the hyperparameter tuning values correspond to an n-leaf parameter and a number of neighbors parameter (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify a plurality of classification model pipelines comprising different combinations of preprocessing techniques, classification model algorithms comprise a k-nearest neighbor, and hyperparameter tuning values correspond to an n-leaf parameter and a number of neighbors parameter)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein when the classification algorithm is a SVM algorithm, the hyperparameter tuning values correspond to a c-parameter and a gamma parameter (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify a plurality of classification model pipelines comprising different combinations of preprocessing techniques, classification model algorithms comprising a support vector machine algorithm, and hyperparameter tuning values correspond to a c-parameter value and a gamma parameter)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 10:
With respect to claim 10, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the performance characteristics comprise response time, CPU usage, memory usage, and classification accuracy (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement by comparing the performance characteristics comprising response time, CPU usage, memory usage, and classification accuracy, to the performance constraints to perform an evaluation to identify whether the trained model meets the performance constraints)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein identifying one of the candidate ML classification model pipelines that meets the performance constraints comprises selecting a candidate ML classification model pipeline associated with an optimal classification accuracy (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an opinion by the user to identify a pipeline associated with an “optimal” classification accuracy)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 12:
With respect to claim 12, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
periodically updates the performance constraints, the training dataset, and the testing dataset (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify updates to performance constraints, the training dataset, and the testing dataset)
for each candidate classification model pipeline:
process the updated training dataset using the data processing techniques (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the training dataset to perform a preprocessing of data according to a preprocessing technique)
compares the performance characteristics to the plurality of performance constraints to identify whether the trained model meets the performance constraints (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement by comparing the performance characteristics to the performance constraints to perform an evaluation to identify whether the trained model meets the performance constraints)
identifies one of the candidate classification model pipelines that meets the updated performance constraints (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify one of the candidate classification model pipelines that meets the performance constraints)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
trains the classification model algorithm on the updated training dataset
tunes the trained classification model algorithm using the hyperparameter tuning values
executes the trained classification model using the updated testing dataset as input to determine performance characteristics for the trained model
builds a new production classification model based upon the identified candidate model pipeline
The training, tuning, testing, and building a model elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim recites the additional elements:
deploys the new production classification model in the production computing environment
These limitations amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
trains the classification model algorithm on the updated training dataset
tunes the trained classification model algorithm using the hyperparameter tuning values
executes the trained classification model using the updated testing dataset as input to determine performance characteristics for the trained model
builds a new production classification model based upon the identified candidate model pipeline
The training, tuning, testing, and building a model elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim recites the additional elements:
deploys the new production classification model in the production computing environment
These limitations amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claims 13-24:
With respect to claims 13-24, the claims recite the limitations substantially similar to those in claims 1-12, respectively. The analysis of claims 1-12 are incorporated herein by reference.
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.
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.
Claims 1, 3, 12-13, 15, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Eban et al. (US 2019/0266513, published 29 August 2019, hereafter Eban) and further in view of Cmielowski et al. (US 2024/0070520, published 29 February 2024, hereafter Cmielowski).
As per independent claim 1, Eban discloses a system for constraint-based optimization of machine learning classification models, the system comprising a server computing device having a memory that stores computer-executable instructions and a processor that executes the computer-executable instructions (Figure 3A) to:
determine performance constraints associated with deployment and execution of a machine learning classification model (paragraph 0027: Here, a machine learning classification model is trained to satisfy a plurality of constraints)
identify a plurality of candidate classification model pipelines, each pipeline comprising a different combination of a classification model algorithm and hyperparameter tuning values (Figures 1-2; paragraphs 0052-0053: Here, a plurality of candidate models are generated using quantile estimators)
for each candidate classification model pipeline:
training the classification model algorithm on the training dataset (paragraph 0011: Here, a classification model is trained using a training dataset)
tuning the trained classification model algorithm using the hyperparameter tuning values (paragraph 0012: Here, a plurality of learnable parameters are updated (tuned) based on the plurality of training iterations)
executing the trained classification model using a testing dataset as input to determine performance characteristics for the trained model (paragraph 0152: Here, the trained model is tested using a testing dataset)
comparing the performance characteristics to the performance constraints to identify whether the trained model meets the performance constraints (paragraph 0191: Here, based upon a determination of whether a criteria is met or not, the training either performs another iteration or concludes)
identify one of the candidate classification model pipelines that meets the performance constraints (paragraph 0191: Here, based upon a determination of whether a criteria is met or not, the training either performs another iteration or concludes)
build a production classification model based upon the identified candidate model pipeline (Figure 6; paragraphs 0024 and 0182: Here, a trained machine learning model is deployed for use)
deploy the production classification model in a production computing environment (Figure 6; paragraphs 0024 and 0182: Here, a trained machine learning model is deployed for use)
Eban fails to specifically disclose:
identify a plurality of candidate classification model pipelines, each pipeline comprising a different combination of data preprocessing techniques, a classification model algorithm, and hyperparameter tuning values
processing the training dataset using the data preprocessing techniques
However, Cmielowski, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses:
identify a plurality of candidate classification model pipelines, each pipeline comprising a different combination of data preprocessing techniques (paragraph 0013: Here, the preprocessing techniques include imputation, encoding, and scaling strategies to select optimal preprocessing techniques)
processing the training dataset using the data preprocessing techniques (paragraph 0013: Here, the preprocessing techniques include imputation, encoding, and scaling strategies to select optimal preprocessing techniques)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combine Cmielowski with Eban, with a reasonable expectation of success, as it would have allowed for identifying and applying optimal preprocessing techniques for use in generating a machine learning model (Cmielowski: paragraph 0013).
As per dependent claim 3, Eban and Cmielowski disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Cmielowski discloses wherein the preprocessing algorithm comprises an imputation step, a feature scaling step, and an encoding step (paragraph 0013: Here, the preprocessing techniques include imputation, encoding, and scaling strategies to select optimal preprocessing techniques)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combine Cmielowski with Eban, with a reasonable expectation of success, as it would have allowed for identifying and applying optimal preprocessing techniques for use in generating a machine learning model (Cmielowski: paragraph 0013).
As per dependent claim 12, Eban and Cmielowski disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Eban discloses wherein:
train using the performance constraints, the training dataset, and the testing dataset for each candidate classification model pipeline (paragraphs 0011-0012 and 0152)
trains the classification model algorithm on the updated training dataset (paragraph 0011: Here, a classification model is trained using a training dataset)
tunes the trained classification model algorithm using the hyperparameter tuning values (paragraph 0012: Here, a plurality of learnable parameters are updated (tuned) based on the plurality of training iterations)
executes the trained classification model using the updated testing dataset as input to determine performance characteristics for the trained model (paragraph 0152: Here, the trained model is tested using a testing dataset)
compares the performance characteristics to the plurality of performance constraints to identify whether the trained model meets the performance constraints (paragraph 0191: Here, based upon a determination of whether a criteria is met or not, the training either performs another iteration or concludes)
identifies one of the candidate classification model pipelines that meets the updated performance constraints (paragraph 0191: Here, based upon a determination of whether a criteria is met or not, the training either performs another iteration or concludes)
builds a new production classification model based upon the identified candidate model pipeline (Figure 6; paragraphs 0024 and 0182: Here, a trained machine learning model is deployed for use)
deploys the new production classification model in the production computing environment (Figure 6; paragraphs 0024 and 0182: Here, a trained machine learning model is deployed for use)
Eban fails to specifically disclose:
periodically updates the performance constraints, the training dataset, and the testing dataset
for each candidate classification model pipeline: process the updated training dataset using the data preprocessing techniques
However, Cmielowski, which is analogous to the claimed invention because it is directed to training a machine learning model, discloses:
periodically updates the training of the machine learning model (paragraph 0058: Here, if a model quality drops below a predetermined quality threshold, the training orchestrator forces a restart of the training process)
for each candidate classification model pipeline: process the updated training dataset using the data preprocessing techniques (paragraph 0013: Here, the preprocessing techniques include imputation, encoding, and scaling strategies to select optimal preprocessing techniques)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combine Cmielowski with Eban, with a reasonable expectation of success, as it would have allowed for identifying and applying optimal preprocessing techniques for use in generating a machine learning model (Cmielowski: paragraph 0013).
With respect to claims 13, 15, and 24, the claims recite the limitations substantially similar to those in claims 1, 3, and 12, respectively. Claims 13, 15, and 24 are similarly rejected.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Iyer et al. (US 2024/0403154, published 5 December 2024, hereafter Iyer) and further in view of Lee (US 2024/0080245, filed 5 May 2023) and further in view of Tong (US 12387132, filed 29 September 2020).
As per dependent claim 2, Eban and Cmielowski disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Eban fails to specifically disclose wherein the performance constraints comprise a maximum response time, a maximum CPU usage, a maximum memory usage, and a maximum platform execution cost.
However, Iyer, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the performance constraints comprise a maximum CPU usage and a maximum memory usage (paragraph 0100: Here, a plurality of constraints are implemented including maximum processor (CPU) usage and maximum memory usage). It would have been obvious to one ordinary skill in the art at the time of the applicant’s effective filing date to have combined Iyer with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for limiting excessive usage by the model (Iyer: paragraph 0100).
Further, Lee, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the performance constraint comprises a maximum response time (paragraph 0094: Here, parameters, including a maximum response time, are used in training the model). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lee with Eban-Cmielowski-Iyer, with a reasonable expectation of success, as it would have allowed for training using parameters to improve the model with respect to response time (Lee: paragraph 0094).
Finally, Tong, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the performance constraint comprises a maximum platform execution cost (column 14, lines 3-26: Here, cost constraints, such as maximum platform cost for executing the machine learning pipelines are defined). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Tong with Eban-Cmielowski-Iyer-Lee, with a reasonable expectation of success, as it would have allowed for controlling costs when generating a machine learning model (Tong: column 14, lines 3-26).
With respect to claim 14, the claims recite the limitations substantially similar to those in claim 2. Claim 14 is similarly rejected.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Ma et al. (US 2025/0036924, 371 date 28 August 2023, hereafter Ma).
As per dependent claim 4, Eban and Cmielowski disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Eban fails to specifically disclose wherein the imputation step comprises mean imputation or median imputation.
However, Ma, which is analogous to the claimed invention because it is directed toward preprocessing for training a machine learning model, discloses wherein the imputation step comprises mean imputation (paragraph 0005) or median imputation. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ma with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for imputing data based upon a mean imputation to account for missing data (Ma: paragraph 0005).
With respect to claim 16, the claim recites the limitations substantially similar to those in claim 4. Claim 16 is similarly rejected.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Fu et al. (US 2025/0059864, filed 15 August 2023, hereafter Fu).
As per dependent claim 5, Eban and Cmielowski disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Eban fails to specifically disclose wherein the feature scaling step comprises standardization or normalization.
However, Fu, which is analogous to the claimed invention because it is directed toward preprocessing for training a machine learning model, discloses wherein the feature scaling step comprises standardization or normalization (paragraph 0068). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Fu with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for modifying the dataset to improve the set for training of the machine learning model (Fu: paragraph 0068).
With respect to claim 17, claim recites the limitations substantially similar to those in claim 5. Claim 17 is similarly rejected.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Wuest et al. (US 2025/0094585, filed 19 September 2023, hereafter Wuest).
As per dependent claim 6, Eban and Cmielowski disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Eban fails to specifically disclose wherein the encoding step comprises one-hot encoding or dummy encoding.
However, Wuest, which is analogous to the claimed invention because it is directed toward preprocessing for training a machine learning model, discloses wherein the encoding step comprises one-hot encoding (paragraph 0068) or dummy encoding. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wuest with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for improving prediction accuracy of a model (Wuest: paragraph 0068).
With respect to claim 18, claim recites the limitations substantially similar to those in claim 6. Claim 18 is similarly rejected.
Claims 7, 9, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Xiao et al. (US 2025/0053853, filed 10 August 2023, hereafter Xiao).
As per dependent claim 7, Eban and Cmielowski disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein by reference. Eban fails to specifically disclose wherein the classification algorithm comprises a k-nearest neighbor (KNN) algorithm or a support vector machine (SVM) algorithm.
However, Xiao, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the classification algorithm comprises a k-nearest neighbor (KNN) algorithm or a support vector machine (SVM) algorithm (paragraph 0048). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Xiao with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for exploring to identify value combinations that improve or optimize the degree to which an ML model can minimize or maximize the aforementioned objective function (Xiao: paragraph 0049).
As per dependent claim 9, Eban, Cmielowski, and Xiao disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein by reference. Xiao discloses wherein the classification algorithm is a SVM algorithm, the hyperparameter tuning values correspond to a c-parameter and a gamma parameter (paragraph 0048). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Xiao with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for exploring to identify value combinations that improve or optimize the degree to which an ML model can minimize or maximize the aforementioned objective function (Xiao: paragraph 0049).
With respect to claims 19 and 21, the claims recite the limitations substantially similar to those in claims 7 and 9, respectively. Claims 19 and 21 are similarly rejected.
Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Eban, Cmielowski, and Xiao and further in view of Ribnick et al. (WO 2012/054225, published 26 April 2012, hereafter Ribnick).
As per dependent claim 8, Eban, Cmielowski, and Xiao disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein by reference. Eban fails to specifically disclose wherein the classification model algorithm is a KNN algorithm and the hyperparameters correspond to an n-leaf parameter and a number of neighbors parameter.
However, Ribnick, which is analogous to the claimed invention because it is directed toward KNNs, discloses, wherein the classification model algorithm is a KNN algorithm and the hyperparameters correspond to an n-leaf parameter (paragraph 0067) and a number of neighbors parameter (paragraph 0064). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ribnick with Eban- Cmielowski-Xiao, with a reasonable expectation of success, as it would have allowed for a more efficient kNN search (Ribnick: paragraph 0067).
With respect to claim 20, claim recites the limitations substantially similar to those in claim 8. Claim 20 is similarly rejected.
Claims 10-11, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Eban and Cmielowski and further in view of Iyer and further in view of Lee.
As per dependent claim 10, Eban and Cmielowski disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Eban discloses wherein the performance characteristics comprise classification accuracy (paragraph 0007).
Eban fails to specifically disclose wherein the performance characteristics comprise response time, CPU usage, and memory usage.
However, Iyer, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the performance characteristics comprise a CPU usage and a memory usage (paragraph 0100: Here, a plurality of constraints are implemented including maximum processor (CPU) usage and maximum memory usage). It would have been obvious to one ordinary skill in the art at the time of the applicant’s effective filing date to have combined Iyer with Eban-Cmielowski, with a reasonable expectation of success, as it would have allowed for limiting excessive usage by the model (Iyer: paragraph 0100).
Further, Lee, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the performance characteristic comprises a response time (paragraph 0094: Here, parameters, including a maximum response time, are used in training the model). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lee with Eban-Cmielowski-Iyer, with a reasonable expectation of success, as it would have allowed for training using parameters to improve the model with respect to response time (Lee: paragraph 0094).
As per dependent claim 11, Eban, Cmielowski, Iyer, and Lee disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein by reference. Eban disclose wherein identifying one of the candidate ML classification model pipelines that meets the performance constraints comprises selectin a candidate ML classification model pipeline associated with an optimal classification accuracy (paragraph 0007).
With respect to claims 22-23, the claims recite the limitations substantially similar to those in claims 10-11, respectively. Claims 22-23 are similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kwon et al. (US 2026/0162344): Discloses imputation means for substituting for missing values (paragraph 0045).
Priyani et al. (US 2025/0232208): Discloses support vector machines using hyperparameter tuning including c-parameters and gamma parameters (paragraph 0008).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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