DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. Applicant’s submission filed 06 May 2026 [hereinafter Response] has been entered, where:
Claims 1 and 9 have been amended.
New claims 17-20 are presented for examination.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a “method,” which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “generating . . . a first plurality of generation indices based on a plurality of features of the learning data,” “determining . . . model accuracy for each of the first plurality of machine learning models,” “performing . . . model selection to select models of a predetermined number having highest model accuracy from the first plurality of machine learning models,” “performing . . . indices generation to generate a second plurality of generation indices based on a second plurality of features,” “performing . . . model accuracy determination to determine model accuracy for each of the second plurality of machine learning models,” “iteratively performing . . . model accuracy determination until a machine learning model having a model accuracy that surpasses and accuracy threshold is generated,” and “selecting the machine learning model having highest model accuracy from the second plurality of machine learning models for deployment.” These activities of “generating,” determining,” “performing,” “iteratively performing,” and “selecting,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) subsection III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of “performing . . . indices generation,” such that “wherein the second plurality of features is derived by performing genetic crossover of the features from generation indices that are associated with the models of the predetermined number,” and “wherein the performing the genetic crossover comprises providing a list of input features to be used at each operation during optimization processing, a number of iterations per trial, and a number of results inherited for subsequent optimization process, and adjusting crossover rates used in the genetic crossover so that features resulting in more accurate models are inherited more frequently in next-generation features,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “iteratively performing,” “wherein the iteratively performing comprises a first iterative loop that optimizes input features using the genetic crossover until a first prescribed number of iterations has been met, and a second iterative loop that optimizes hyperparameters and generates an updated model until a second prescribed number of iterations has been met, wherein the first iterative loop and the second iterative loop are performed iteratively in an outer loop until a third prescribed number of iterations has been met,” and accordingly, are merely more specific to the abstract idea. Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a processor,” which is a generic computer component used to implement the abstract idea that do not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)).
The claim also recites additional elements of “a first plurality of machine learning models,” and “a second plurality of machine learning models,” which are generic computer components used to implement the abstract idea, and does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). Also, these limitations generally link the use of the judicial exception to the particular technological environment or field of use pertaining to generation and selection of “models,” (MPEP § 2106.05(h)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “generating a first plurality of machine learning models trained with the learning data and the first plurality of generation indices,” “performing machine learning model generation to generate a second plurality of machine learning models trained with the learning data and the second plurality of feature,“ and “iteratively performing model selection, indices generation by performing genetic crossover with features from indices of preceding iteration, machine learning model generation, . . .” These limitations recite the use of generic components (a first plurality of machine learning models, a second plurality of machine learning models) to implement the abstract idea, and do not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)).
The claim also recites more specifics or details of the additional element of “generating . . . models trained,” “wherein each of the first plurality of machine learning models is trained with a respective generation index of the first plurality of generation indices,” and “wherein each of the second plurality of machine learning models is trained with a unique combination of features from the second plurality of features,” and accordingly, are merely more specific to the additional element. The claim also recites the additional elements of “obtaining . . . learning data to be used in machine learning model training,” “performing, by the processor, data validation and generating configuration files required for a deep framework,” and organizing, by the processor, the learning data for training, evaluation, and testing,” which are pre-processing, insignificant extra-solution activities of mere data gathering and data preparation, (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “performing . . . data validation,” “wherein the deep framework builds deep learning models for production without requiring generation of additional code,” and accordingly, is merely more specific to the additional element.
Moreover, the limitation of “generates an updated model” is the use of generic computer components (model) to implement the abstract idea, (MPEP § 2106.5(f)), that does not serve to integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “a processor,” which is a generic computer component used to implement the abstract idea that do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)).
The claim also recites additional elements of “a first plurality of machine learning models,” and “a second plurality of machine learning models,” which are generic computer components used to implement the abstract idea, and does not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). Also, these limitations generally link the use of the judicial exception to the particular technological environment or field of use pertaining to generation and selection of “models,” (MPEP § 2106.05(h)), that does not amount to significantly more than the abstract idea.
The claim also recites limitations of “generating a first plurality of machine learning models trained with the learning data and the first plurality of generation indices,” “performing machine learning model generation to generate a second plurality of machine learning models trained with the learning data and the second plurality of feature,“ and “iteratively performing model selection, indices generation by performing genetic crossover with features from indices of preceding iteration, machine learning model generation, . . .” These limitations recite the use of generic components (a first plurality of machine learning models, a second plurality of machine learning models) to implement the abstract idea, and do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)).
The claim also recites more specifics or details of the additional element of “generating . . . models trained,” “wherein each of the first plurality of machine learning models is trained with a respective generation index of the first plurality of generation indices,” and “wherein each of the second plurality of machine learning models is trained with a unique combination of features from the second plurality of features,” and accordingly, are merely more specific to the additional element. The claim also recites the additional elements of “obtaining . . . learning data to be used in machine learning model training,” “performing, by the processor, data validation and generating configuration files required for a deep framework,” and organizing, by the processor, the learning data for training, evaluation, and testing,” which are well-understood, routine, and conventional activity of storing, formatting, and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics to the additional element of “performing . . . data validation,” “wherein the deep framework builds deep learning models for production without requiring generation of additional code,” and accordingly, is merely more specific to the additional element.
Moreover, the limitation of “generates an updated model” is the use of generic computer components (model) to implement the abstract idea, (MPEP § 2106.5(f)), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is subject matter ineligible.
Claim 9 recites a “non-transitory computer readable medium,” which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “generating . . . a first plurality of generation indices based on a plurality of features of the learning data,” “determining . . . model accuracy for each of the first plurality of machine learning models,” “performing . . . model selection to select models of a predetermined number having highest model accuracy from the first plurality of machine learning models,” “performing . . . indices generation to generate a second plurality of generation indices based on a second plurality of features,” “performing . . . model accuracy determination to determine model accuracy for each of the second plurality of machine learning models,” “iteratively performing . . . model accuracy determination until a machine learning model having a model accuracy that surpasses and accuracy threshold is generated,” and “selecting the machine learning model having highest model accuracy from the second plurality of machine learning models for deployment.” These activities of “generating,” determining,” “performing,” “iteratively performing,” and “selecting,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) subsection III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of “performing . . . indices generation,” such that “wherein the second plurality of features is derived by performing genetic crossover of the features from generation indices that are associated with the models of the predetermined number,” and “wherein the performing the genetic crossover comprises providing a list of input features to be used at each operation during optimization processing, a number of iterations per trial, and a number of results inherited for subsequent optimization process, and adjusting crossover rates used in the genetic crossover so that features resulting in more accurate models are inherited more frequently in next-generation features,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “iteratively performing,” “wherein the iteratively performing comprises a first iterative loop that optimizes input features using the genetic crossover until a first prescribed number of iterations has been met, and a second iterative loop that optimizes hyperparameters and generates an updated model until a second prescribed number of iterations has been met, wherein the first iterative loop and the second iterative loop are performed iteratively in an outer loop until a third prescribed number of iterations has been met,” and accordingly, are merely more specific to the abstract idea. Thus, claim 9 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a processor,” which is a generic computer component used to implement the abstract idea that do not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites additional elements of “a first plurality of machine learning models,” and “a second plurality of machine learning models,” which are generic computer components used to implement the abstract idea, and does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). Also, these limitations generally link the use of the judicial exception to the particular technological environment or field of use pertaining to generation and selection of “models,” (MPEP § 2106.05(h)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “generating a first plurality of machine learning models trained with the learning data and the first plurality of generation indices,” “performing machine learning model generation to generate a second plurality of machine learning models trained with the learning data and the second plurality of feature,“ and “iteratively performing model selection, indices generation by performing genetic crossover with features from indices of preceding iteration, machine learning model generation, . . .” These limitations recite the use of generic components (a first plurality of machine learning models, a second plurality of machine learning models) to implement the abstract idea, and do not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites more specifics or details of the additional element of “generating . . . models trained,” “wherein each of the first plurality of machine learning models is trained with a respective generation index of the first plurality of generation indices,” and “wherein each of the second plurality of machine learning models is trained with a unique combination of features from the second plurality of features,” and accordingly, are merely more specific to the additional element.
The claim also recites the additional elements of “obtaining . . . learning data to be used in machine learning model training,” “performing, by the processor, data validation and generating configuration files required for a deep framework,” and organizing, by the processor, the learning data for training, evaluation, and testing,” which are pre-processing, insignificant extra-solution activities of mere data gathering and data preparation, (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “performing . . . data validation,” “wherein the deep framework builds deep learning models for production without requiring generation of additional code,” and accordingly, is merely more specific to the additional element.
Moreover, the limitation of “generates an updated model” is the use of generic computer components (model) to implement the abstract idea, (MPEP § 2106.5(f)), that does not serve to integrate the abstract idea into a practical application. Therefore, claim 9 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “a processor,” which is a generic computer component used to implement the abstract idea that do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)).
The claim also recites additional elements of “a first plurality of machine learning models,” and “a second plurality of machine learning models,” which are generic computer components used to implement the abstract idea, and does not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). Also, these limitations generally link the use of the judicial exception to the particular technological environment or field of use pertaining to generation and selection of “models,” (MPEP § 2106.05(h)), that does not amount to significantly more than the abstract idea. The claim also recites limitations of “generating a first plurality of machine learning models trained with the learning data and the first plurality of generation indices,” “performing machine learning model generation to generate a second plurality of machine learning models trained with the learning data and the second plurality of feature,“ and “iteratively performing model selection, indices generation by performing genetic crossover with features from indices of preceding iteration, machine learning model generation, . . .” These limitations recite the use of generic components (a first plurality of machine learning models, a second plurality of machine learning models) to implement the abstract idea, and do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim also recites more specifics or details of the additional element of “generating . . . models trained,” “wherein each of the first plurality of machine learning models is trained with a respective generation index of the first plurality of generation indices,” and “wherein each of the second plurality of machine learning models is trained with a unique combination of features from the second plurality of features,” and accordingly, are merely more specific to the additional element.
The claim also recites the additional elements of “obtaining . . . learning data to be used in machine learning model training,” “performing, by the processor, data validation and generating configuration files required for a deep framework,” and organizing, by the processor, the learning data for training, evaluation, and testing,” which are well-understood, routine, and conventional activity of storing, formatting, and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea. The claim also recites more details or specifics to the additional element of “performing . . . data validation,” “wherein the deep framework builds deep learning models for production without requiring generation of additional code,” and accordingly, is merely more specific to the additional element.
Moreover, the limitation of “generates an updated model” is the use of generic computer components (model) to implement the abstract idea, (MPEP § 2106.5(f)), that does not amount to significantly more than the abstract idea. Therefore, claim 9 is subject matter ineligible.
Claims 2, 3, and 4 depend directly or indirectly from claim 1. Claims 10, 11, and 12 depend directly or indirectly from claim 9. The claims recite more details or specifics to the abstract idea of “generating . . . a first plurality of generation indices,” (claims 2 and 10: “wherein the first plurality of generation indices comprises generation indices specifying the plurality of features of the learning data”; claims 3 and 11: “wherein the first plurality of generation indices further comprises at least one of generation indices specifying structure of machine learning model to be generated, generation indices specifying training method of machine learning model associated with a feature, or generation indices specifying model type of machine learning model to be generated”; claims 4 and 12: “wherein the first plurality of generation indices further comprises at least one of generation indices specifying number of intermediary layers to be included in a machine learning model, generation indices specifying number of nodes to be included in each of the intermediary layers, or generation indices specifying node connection of the number of nodes’), and accordingly, are simply more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Accordingly, claims 2-4 and 10-12 are subject-matter ineligible.
Claim 5 depends directly or indirectly from claim 1. Claim 13 depends directly or indirectly from claim 9. The claims recite more specifics or details to the abstract idea of “determining . . . model accuracy,” (claims 5 and 13: wherein determining model accuracy for each of the first plurality of machine learning models comprises evaluating model accuracy for each of the first plurality of machine learning models using the evaluation data”), and accordingly, are merely more specific to the abstract idea. Also, the claim recites more specifics or details to the additional elements of “obtaining . . . learning data,” (claims 5 and 13: “wherein the learning data is split into training data and evaluation data”), and the additional element of “generating the first plurality of machine learning models,” (claims 5 and 13: “wherein generating the first plurality of machine learning models comprises training the first plurality of machine learning models with the training data and the plurality of features of the learning data”), which are each merely more specific to the respective additional element. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Accordingly, claims 5 and 13 are subject-matter ineligible.
Claims 6-8 depend directly or indirectly from claim 1. Claims 14-16 depend directly or indirectly from claim 9. The claims recite more details or specifics of the additional element of “obtaining learning data,” (claims 6 and 14: “wherein the plurality of features of the learning data are statistical features of the learning data”; claims 7 and 15: “wherein the learning data comprises one of integers, floating-point numbers, or strings”; claims 8 and 16: “wherein the learning data comprises integers . . . .”), and accordingly, are merely more specific to the additional element. Also, the claims recite more details or specifics of the abstract idea of “generating a first plurality of generation indices,” (claims 8 and 16: “wherein . . . and the first plurality of generation indices is generated based on contiguity of the learning data”), and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Accordingly, claims 6-8 and 14-16 are subject-matter ineligible.
Claim 17 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “iteratively performing,” “wherein the second iterative loop optimizes the hyperparameters using at least one of a Bayesian optimization algorithm or a random search algorithm,” and accordingly, is merely more specific to the abstract idea. Therefore, claim 17 is subject-matter ineligible.
Claim 18 depends directly or indirectly from claim 1. The claim further recites “executing a feature function selection algorithm that selects feature functions based on a data type of the learning data, a density of the learning data, and an amount of the learning data.” The activity of “executing . . . [an] algorithm that selects” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) subsection III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Therefore, claim 18 is subject-matter ineligible.
Claim 19 depends directly or indirectly from claim 1. The claim further recites “determining, based on the training and the evaluation, whether an overfitting condition or an underfitting condition exists,” in which the activity of “determining” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) subsection III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea.
Under Step 2A Prong Two, the claim recites “modifying the configuration files in response to the determining,” which is the insignificant extra-solution activity of storing data, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Under Step 2B, “modifying the configuration files in response to the determining” is the well-understood, routine, and conventional activity of storing data in memory, (MPEP § 2106.04(d) sub II.iv), that does not amount to significantly more than the abstract idea. Therefore, claim 19 is subject-matter ineligible.
Claim 20 depends directly or indirectly from claim 1. The claim further recites “wherein the iteratively performing further comprises generating a best configuration file for optimizing the machine learning model,” and “generating . . . a report based on the iteratively performing,” which under Step 2A Prong Two are insignificant extra-solution activities of providing a result of the iteratively performing, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Under Step 2B, these additional limitations are well-understood, routine, and conventional activities of storing information in memory, (MPEP § 2106.04(d) sub II.iv), and of providing an output, (MPEP § 2106.04(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 20 is subject-matter ineligible..
Response to Arguments
5. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
35 U.S.C. § 101
6. Under Section 101, “Applicant respectfully submits that the claims as amended recite features that cannot practically be performed in the human mind. Specifically, amended claims 1 and 9 now recite
"wherein the iteratively performing comprises a first iterative loop that optimizes input features using the genetic crossover until a first prescribed number of iterations has been met, and a second iterative loop that optimizes hyperparameters and generates an updated model until a second prescribed number of iterations has been met, wherein the first iterative loop and the second iterative loop are performed iteratively in an outer loop until a third prescribed number of iterations has been met."
This nested three-loop computational architecture, comprising a first iterative loop for input feature optimization, a second iterative loop for hyperparameter optimization and model generation, and an outer loop iterating both, requires automated machine processing and cannot practically be performed in the human mind.” (Response at p. 8).
Examiner Response:
Examiner respectfully disagrees because, under Step 2A Prong One, the rejection identifies the judicial exception (that is, abstract idea) by referring to what is recited (that is, set forth or described) in the claim and explain why it is considered an exception. For example, if the claim is directed to an abstract idea, the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim and explain why it is an abstract idea.
For example, the rejection sets out above, inter alia, The activities of “generating,” determining,” “performing,” “iteratively performing,” and “selecting,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) subsection III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
With regard to the assertion that iteratively performing “requires automated machine processing,” The fact that machine processing can be used to make a process more efficient does not necessarily render an abstract idea less abstract. Relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, 573 U.S. at 224 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept); Bancorp Servs. v. Sun Life Assur. Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (a computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”); MPEP § 2106.05(f)(2) (“Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not . . . provide significantly more.”).
Accordingly, the claims recite an abstract idea as set out above in detail.
7. Applicant submits that “[u]nder Step 2A Prong Two, even if the Examiner maintains the abstract idea characterization, the claims as amended integrate any alleged abstract idea into a practical application by reciting a specific improvement to the technology of machine learning model generation. The specification explains that
"the inventor has determined that by adding in the iterative loop of the input optimization and hyper parameter optimization, some critical and unexpected results may be provided."
(Specification, paragraph [00129].) The specification further explains that ‘the tuner framework determines and selects an optimal combination of features, such that the critical information and parameters are selected, and the noise is removed.’ (Specification, paragraph [00128].) The claims recite a specific technical solution, i.e., decoupled, nested iterative optimization of input features and hyperparameters, that improves the functioning of the machine learning model generation process itself, not merely applying an abstract idea on a generic computer. This is analogous to claims found eligible because they recite a specific improvement to computer functionality.” (Response at pp. 8-9).
In regard to Desjardins, Applicant submits the “Office Action's characterization of the machine learning model generation and optimization steps as ‘mental processes’ and the machine learning models as ‘generic computer components’ (Office Action, pp. 3-5) is inconsistent with the guidance provided in Desjardins, which instructs that claims should not be evaluated at such a high level of generality. As in Desjardins, the claims here recite specific improvements to how machine learning models are generated and optimized, namely, through the nested iterative loop architecture for decoupled input feature and hyperparameter optimization, and thus integrate any abstract idea into a practical application.” (Response at p. 9).
That is, the “Office Action acknowledged in the Response to Arguments section that the specification ‘appears to set out an improvement directed to model building/ selection’ but found it ‘conclusory.’ (Office Action, p. 28.). appears to set out an improvement directed to model building/ selection" but found it "conclusory." (Office Action, p. 28.) The amended claims now explicitly recite the specific nested iterative loop architecture that implements this improvement, thereby reflecting the disclosed improvement in the claim language itself This satisfies the requirement that ‘the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.’" (Response at p. 9).
Examiner Response:
Under Step 2A Prong Two, the rejection identifies any additional elements recited in the claim beyond the identified judicial exception (i.e., abstract idea); and evaluate the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)-(c) and (e)-(h).
“Integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
By way of example to Desjardins, the MPEP provides under Step 2A Prong Two that “the [Desjardins] specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the [appeals review panel (ARP)] evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).” (MPEP § 2106.04(d) sub III; see “Advance Notice of Change to the MPEP in light of Ex Parte Desjardins” (05 December 2025) at p. 2)).
Applicant submits that the Examiner having identified additional elements as “generic computer components” is inconsistent with the guidance provided in Desjardins, “which instructs that claims should not be evaluated at such a high level of generality.” As an example, the rejection above identifies the additional elements of a “processor,” and also “a first plurality of machine learning models,” and “a second plurality of machine learning models,” as being recited at a high-level of generality. The instant disclosure confirms the claims do not require specialized computing components. (see Specification ¶ 0061 (“For example, but not by way of limitation, the instructions may be executed on a single processor in a single machine, multiple processors in a single machine, and/or multiple processors in multiple machines”); Specification ¶ 0199 (“Examples of the models include not only SVMs but also neural networks having a plurality of intermediary layers (hidden layers). Neural networks of various types are known, such as a feed-forward DNN in which information is communicated from the input layer to the output layer in one direction, a convolutional neural network (CNN) that performs convolution of information in the intermediary layers, a recurrent neural network (RNN) having a directed cycle, and a Boltzmann machine. These various types of neural networks also include other types of neural networks such as a long short-term memory (LSTM)”)).
Also, the specification does not provide any details to the underlying algorithms and/or optimization of the models, other than “the information providing apparatus 10 optimizes the number of dimensions of the input data that is to be input to the model. For example, by controlling the number of nodes that are included in the input layer of the model, the information providing apparatus 10 optimizes the number of dimension of the input data. To put it in other words, the information providing apparatus 10 optimizes the number of dimensions of the space in which the input data is embedded.” (Specification ¶ 0198).
Also, with regard to the amended language, Applicant submits that as “in Desjardins, the claims here recite specific improvements to how machine learning models are generated and optimized, namely, through the nested iterative loop architecture for decoupled input feature and hyperparameter optimization.” However, the claims are not so limited. Though the claim recites “a first iterative loop” and “a second iterative loop” performed in “an outer loop,” the configuration does not specify being limited to a nested loop architecture. The plain and ordinary meaning of a nested loop, an outer loop controls the overall flow, while an inner loop executes completely for each iteration of the outer loop. The claims are not so constrained.
Also, as set out in the Office Action, it appears that the specification appears to set out an improvement directed to model building / selection, but in a conclusory manner.” (see Office Action at p. 28).
Accordingly, the claims are subject-matter ineligible, as set out above in detail.
35 U.S.C. §§ 102 & 103
8. Applicant submits that “Claims 1 and 9 have been amended to recite, inter alia, ‘wherein the iteratively performing comprises a first iterative loop that optimizes input features using the genetic crossover until a first prescribed number of iterations has been met, and a second iterative loop that optimizes hyperparameters and generates an updated model until a second prescribed number of iterations has been met, wherein the first iterative loop and the second iterative loop are performed iteratively in an outer loop until a third prescribed number of iterations has been met.’ Applicant respectfully submits that Andoni '938 does not teach or suggest this feature.
Andoni '938 teaches a genetic algorithm that evolves neural network models over epochs using crossover and mutation operations on model structures such as topology, weights, and activation functions. (Andoni '938, par. [0006]). However, Andoni '938 does not disclose or suggest a decoupled optimization architecture where input feature optimization and hyperparameter optimization are performed in separate iterative loops nested within an outer iterative loop. Andoni '938's genetic algorithm operates as a single evolutionary process on model structures, and it does not separately and iteratively optimize input features in a first loop and hyperparameters in a second loop, with both loops nested in an outer loop. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 102 for claims 1 and 9.” (Response at p. 10).
Examiner’s Response:
Examiner agrees with Applicant’s amendments and arguments thereto. Accordingly, Examiner WITHDRAWS the rejections under Sections 102 and 103.
Conclusion
9. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
10. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20180240041 to Koch et al.) teaches automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.
(Kerschke et al., "Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning," arXiv (2018)) teaches framework interlinks between feature-based problem characterization, benchmarked performances of the considered optimization algorithms, the actual modeling process of the machine learning algorithms (red area in the center) and the evaluation of the algorithm selectors’ performances. The grey box in the background reveals the connections of the aforementioned topics to the field of machine learning in general.
11. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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