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 .
2. Applicant's submission filed on 09 July 2026 [hereinafter Response] has been entered, where:
Claims 1, 7, 8, and 14 have been amended.
Claims 2-6, 9-13, and 15-23 have been cancelled.
Claims 1, 7, 8, 14, and 24-27 are pending.
Claims 1, 7, 8, 14, and 24-27 are rejected.
Information Disclosure Statement
3. Information disclosure statements were submitted on 08 April 2026 and 29 March 2026. The submissions comply with the provisions of 37 CFR 1.97. Accordingly, the Examiner considered the information disclosure statements.
Claim Rejections - 35 U.S.C. § 101
4. 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.
5. Claims 1, 7, 8, 14 and 24-27 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 “[(a)] selecting . . . in a multi-objective optimization, a plurality of metrics for which hyperparameters of a machine-learning model are to be tuned, each metric of the plurality of metrics being configured for a specific objective and associated with a plurality of specification parameters including a target score of the metric, a penalty factor of the metric, and a bonus factor of the metric,” “[(b)] assigning, by the digital assistant builder platform, a first weight to each metric of the plurality of metrics,” “[(c)] configuring the sets of plurality of specification parameters for each metric of the plurality of metrics in accordance with at least one criterion,” “[(d)] evaluating the machine-learning model using one or more validation datasets to obtain a metric score for each metric of the plurality of metrics,” “[(e)] formulating, by the digital assistant builder platform, a weighted loss function using second weights for the plurality of metrics in an asymmetric loss technique based on a difference between the metric score and the target score of each metric, and the penalty factor or the bonus factor,” “[(f)] establishing, by the digital assistant builder platform, one or more constraints for the machine-learning model based on one or more hyperparameters,” and “[(g)] iteratively tuning, by the digital assistant builder platform, the hyperparameters associated with the machine-learning model to obtain a minimum value of the weighted loss function across the plurality of metrics.” These activities of “[(a)] selecting,” [(b)] assigning,” “[(c)] configuring,” “[(d)] evaluating” “[(e)] formulating,” “[(f)] establishing . . . constraints,” and “[(g)] tuning” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, recite a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim provides more details or specifics to the abstract idea of “[(a)] selecting a plurality of metrics,” “[(a.1)] wherein the plurality of metrics is selected to include a regression error of the machine-learning model and at least one from among a size of the machine-learning model, a training time of the machine-learning model, an accuracy of the machine-learning model, a stability of the machine-learning model, and a confidence score of the machine-learning model,” and accordingly, is merely more specific to the abstract idea.
The claim recites more details or specifics to the abstract idea of “[(b)] assigning,” “[(b.1)] wherein the first weight specifies an importance of the metric in evaluating a performance of the machine-learning model,” and “[(c)] configuring,” in that “[(c.1)] the configuring further comprises: assigning , for a corresponding set of the plurality of specification parameters associated with the metric for the regression error, a first target score, a first penalty factor value that is a relatively high value, and a first bonus factor value that is a relatively low value lower than the first penalty factor value, for minimizing the regression error of the machine-learning model,” and “[(e)] formulating” “[(e.1)] wherein the penalty factor and the bonus factor are related to a second weight associated with each metric, and, in the asymmetric loss technique, the penalty factor is used as a higher value for the second weight associated with the metric failing the target value and the bonus factor is used as a lower value for the second weight associated with the metric exceeding the target value,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(f)] establishing . . . constraints,” “[(f.1)] wherein the one or more constraints include at least one from among a maximum model size, a maximum inference latency, and a maximum training time,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(g)] iteratively tuning the hyperparameters,” “comprises: [(g)](a) selecting initial values for the hyperparameters, the machine-learning model being configured based on the initial values; [(g)](b) evaluating the weighted loss function using the initial values of the hyperparameters; [(g)](c) determining whether the weighted loss function is optimized based on the evaluating; and [(g)](d) in response to the weighted loss function not being optimized, searching for new values for the hyperparameters, reconfiguring the machine-learning model with the new values, and repeating steps (b) and (c) using the reconfigured machine-learning model), and accordingly, are merely more specific to the abstract idea.
The claim also recites the limitations of “[(k.1)] in response to determining, for the regression error, that the metric score is greater than the first target score, assigning to the difference between the metric score and the first target score, the first bonus factor value as a weight associated with the metric, and [(k.2)] in response to determining that the metric score is lower than the first target score, assigning to the difference between the first target score and the metric score, the first penalty factor value as the weight associated with the metric,” which contain limitations that 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) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). 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 abstract idea include “a machine learning model,” “a digital assistant builder platform,” and “a digital assistant chatbot system (DACS),” and a “network,” which are recited at a high-level of generality, and accordingly, are generic computer components in which the abstract ideas are applied, (MPEP § 2106.05(f)), and does not integrate the abstract idea into a practical application.
The claim also recites the limitation of “[(h)] in response to the weighted loss function being optimized and each constraint being satisfied, generating, by the digital assistant builder platform, a validated machine-learning model comprising the tuned hyperparameters, with values for the hyperparameters obtained in a last iteration,” which is the use of the generic computer component (by the digital assistant builder platform) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim further recites the limitations of “[(i)] communicating, via a network, by the digital assistant builder platform to a digital assistant chatbot system (DACS) that is configured for a set of predetermined intents, the validated machine-learning model,” and “[(j)] providing by the DACS as an input to the validated machine-learning model, an input utterance received from a user,” where the activities of “[(i)] communicating” and [(j)] providing” are insignificant extra-solution activities of mere receiving of data, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim recites more details or specifics of the additional element of “[(h), (i)] providing,” “[(h.1)] thereby causing the DACS to incorporate the validated machine-learning model,” and “[(i.1)] wherein, based on the input utterance, the validated machine-learning model is configured to output a prediction of an intent from the set of predetermined intents,” and accordingly, are merely more specific to the additional elements. Therefore, claim 1 is directed to the abstract idea. 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 claim recites additional elements beyond the identified abstract idea include “a machine learning model,” and “a digital assistant chatbot system (DACS),” which are recited at a high-level of generality, and accordingly, are generic computer components in which the abstract ideas are applied, (MPEP § 2106.05(f)), and does not amount to significantly more than the abstract idea. The claim also recites the limitation of “[(h)] in response to the weighted loss function being optimized and each constraint being satisfied, generating, by the digital assistant builder platform, a validated machine-learning model comprising the tuned hyperparameters, with values for the hyperparameters obtained in a last iteration,” which is the use of the generic computer component (by the digital assistant builder platform) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim further recites the limitations of “[(i)] communicating, via a network, by the digital assistant builder platform to a digital assistant chatbot system (DACS) that is configured for a set of predetermined intents, the validated machine-learning model,” and “[(j)] providing by the DACS as an input to the validated machine-learning model, an input utterance received from a user,” where the activities of “[ (i)] communicating,” and [(j)] providing” are well-understood, routine, and conventional activities of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim recites more details or specifics of the additional element of “[(h), (i)] providing,” “[(h.1)] thereby causing the DACS to incorporate the validated machine-learning model,” and “[(i.1)] wherein, based on the input utterance, the validated machine-learning model is configured to output a prediction of an intent from the set of predetermined intents,” and accordingly, are merely more specific to the additional elements. Thus, claim 1 is subject-matter ineligible.
Claim 8 recites a system, which is a machine, 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 “[(a)] selecting . . . in a multi-objective optimization, a plurality of metrics for which hyperparameters of a machine-learning model are to be tuned, each metric of the plurality of metrics being configured for a specific objective and associated with a plurality of specification parameters including a target score of the metric, a penalty factor of the metric, and a bonus factor of the metric,” “[(b)] assigning, by the digital assistant builder platform, a first weight to each metric of the plurality of metrics,” “[(c)] configuring the sets of plurality of specification parameters for each metric of the plurality of metrics in accordance with at least one criterion,” “[(d)] evaluating the machine-learning model using one or more validation datasets to obtain a metric score for each metric of the plurality of metrics,” “[(e)] formulating, by the digital assistant builder platform, a weighted loss function using second weights for the plurality of metrics in an asymmetric loss technique based on a difference between the metric score and the target score of each metric, and the penalty factor or the bonus factor,” “[(f)] establishing, by the digital assistant builder platform, one or more constraints for the machine-learning model based on one or more hyperparameters,” and “[(g)] iteratively tuning, by the digital assistant builder platform, the hyperparameters associated with the machine-learning model to obtain a minimum value of the weighted loss function across the plurality of metrics.” These activities of “[(a)] selecting,” [(b)] assigning,” “[(c)] configuring,” “[(d)] evaluating” “[(e)] formulating,” “[(f)] establishing . . . constraints,” and “[(g)] tuning” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, recite a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim provides more details or specifics to the abstract idea of “[(a)] selecting a plurality of metrics,” “[(a.1)] wherein the plurality of metrics is selected to include a regression error of the machine-learning model and at least one from among a size of the machine-learning model, a training time of the machine-learning model, an accuracy of the machine-learning model, a stability of the machine-learning model, and a confidence score of the machine-learning model,” and accordingly, is merely more specific to the abstract idea.
The claim recites more details or specifics to the abstract idea of “[(b)] assigning,” “[(b.1)] wherein the first weight specifies an importance of the metric in evaluating a performance of the machine-learning model,” and “[(c)] configuring,” in that “[(c.1)] the configuring further comprises: assigning , for a corresponding set of the plurality of specification parameters associated with the metric for the regression error, a first target score, a first penalty factor value that is a relatively high value, and a first bonus factor value that is a relatively low value lower than the first penalty factor value, for minimizing the regression error of the machine-learning model,” and “[(e)] formulating” “[(e.1)] wherein the penalty factor and the bonus factor are related to a second weight associated with each metric, and, in the asymmetric loss technique, the penalty factor is used as a higher value for the second weight associated with the metric failing the target value and the bonus factor is used as a lower value for the second weight associated with the metric exceeding the target value,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(f)] establishing . . . constraints,” “[(f.1)] wherein the one or more constraints include at least one from among a maximum model size, a maximum inference latency, and a maximum training time,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(g)] iteratively tuning the hyperparameters,” “comprises: [(g)](a) selecting initial values for the hyperparameters, the machine-learning model being configured based on the initial values; [(g)](b) evaluating the weighted loss function using the initial values of the hyperparameters; [(g)](c) determining whether the weighted loss function is optimized based on the evaluating; and [(g)](d) in response to the weighted loss function not being optimized, searching for new values for the hyperparameters, reconfiguring the machine-learning model with the new values, and repeating steps (b) and (c) using the reconfigured machine-learning model), and accordingly, are merely more specific to the abstract idea.
The claim also recites the limitations of “[(k.1)] in response to determining, for the regression error, that the metric score is greater than the first target score, assigning to the difference between the metric score and the first target score, the first bonus factor value as a weight associated with the metric, and [(k.2)] in response to determining that the metric score is lower than the first target score, assigning to the difference between the first target score and the metric score, the first penalty factor value as the weight associated with the metric,” which contain limitations that 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) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). 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 abstract idea include “a processor,” “[a digital assistant builder platform] configured to communicate with the DACS via network and comprising a processor and a memory including instructions that, when executed with the processor, cause the computing device to,” which are generic computer components used to execute computer instructions to apply the abstract idea, (MPEP § 2106.05(f)), and do not integrate the abstract idea into a practical application. The claim also recites the additional element of “a machine learning model,” a “digital assistant builder platform,” a “processor,” and “a digital assistant chatbot system (DACS),” which are recited at a high-level of generality, and accordingly, are generic computer components in which the abstract ideas are applied, (MPEP § 2106.05(f)), and does not integrate the abstract idea into a practical application.
The claim also recites the limitation of “[(h)] in response to the weighted loss function being optimized and each constraint being satisfied, generating, by the digital assistant builder platform, a validated machine-learning model comprising the tuned hyperparameters, with values for the hyperparameters obtained in a last iteration” which is the use of the generic computer component (by the digital assistant builder platform) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim further recites the limitations of “[(i)] providing via the network to the DACS the validated machine-learning model, thereby causing the DACS to incorporate the validated machine-learning model,,” and “[(j) wherein the DACS is configured to perform operations including:] providing, as an input to the validated machine-learning model an input utterance received from a user, ” where the activities of “[(i), (j)] providing” are insignificant extra-solution activities of mere transmitting and receiving of data, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “[(i), (j)] providing,” “[(i.1)] thereby causing the DACS to incorporate the validated machine-learning model,” “[(j.1) wherein, based on the input utterance, the validated machine-learning model is configured to output a prediction of an intent for the input utterance from the set of predetermined intents,” and “[(j.2)] wherein the at least one criterion includes a criterion for minimizing the regression error,” and accordingly, are merely more specific to the additional elements. Therefore, claim 8 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 claim recites additional elements beyond the identified abstract idea include “a processor,” “[a digital assistant builder platform] configured to communicate with the DACS via network and comprising a processor and a memory including instructions that, when executed with the processor, cause the computing device to,” which are generic computer components used to execute instructions to apply the abstract ideas, (MPEP § 2106.05(f)), and does not amount to significantly more than the abstract idea. The claim also recites the additional element of “a machine learning model,” and “a digital assistant chatbot system (DACS),” which are recited at a high-level of generality, and accordingly, are generic computer components in which the abstract ideas are applied, (MPEP § 2106.05(f)), and does not amount to significantly more than the abstract idea.
The claim also recites the limitation of “[(h)] in response to the weighted loss function being optimized and each constraint being satisfied, generating, by the digital assistant builder platform, a validated machine-learning model comprising the tuned hyperparameters, with values for the hyperparameters obtained in a last iteration,” which is the use of the generic computer component (by the digital assistant builder platform) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim further recites the limitations of ““[(i)] providing via the network to the DACS the validated machine-learning model, thereby causing the DACS to incorporate the validated machine-learning model,,” and “[(j) wherein the DACS is configured to perform operations including:] providing, as an input to the validated machine-learning model an input utterance received from a user, ”where the activities of “[(h), (i)] providing” are well-understood, routine, and conventional activities of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “[(i), (j)] providing,” “[(i.1)] thereby causing the DACS to incorporate the validated machine-learning model,”
“[(j.1) wherein, based on the input utterance, the validated machine-learning model is configured to output a prediction of an intent for the input utterance from the set of predetermined intents,” and “[(j.2)] wherein the at least one criterion includes a criterion for minimizing the regression error,” and accordingly, are merely more specific to the additional elements. Thus, claim 8 is subject-matter ineligible.
Claim 7 depends from claim 1. Claim 14 depends from claim 8. The claims recite more details or specifics of the additional element of the “machine-learning model,” (claims 7 and 14: wherein the machine-learning model is a neural network model, and the hyperparameters associated with the machine-learning model include at least two from among a number of layers of the machine-learning model, a learning rate of the machine-learning model, a number of hidden units in each layer of the machine-learning model, a learning algorithm utilized to train the machine-learning model), and accordingly, are merely more specific to the additional element. 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. Accordingly, claims 7 and 14 are subject-matter ineligible.
Claim 24 depends directly or indirectly from claim 1. Claim 26 depends directly or indirectly from claim 8. The claims provide more details or specifics to the abstract idea of “[(f)] establishing,” (claims 24 and 26: “wherein: the establishing includes [(f.2)] establishing a plurality of constraints that include the one or more constraints, [(f.3)] each constraint of the plurality of constraints is assigned a priority value indicating a level of importance,” and accordingly, is merely more specific to the abstract idea.
The claims also provide more details or specifics to the abstract idea of “[(g)] iteratively tuning,” (claims 24 and 26: “wherein . . . [(g)] the iterative tuning further comprises [(g.1)] optimizing the machine-learning model to satisfy the plurality of constraints in an order of respective priority values, such that a constraint assigned a higher priority value is required to be satisfied before a constraint assigned a lower priority value is considered,” and accordingly, is merely more specific to the abstract idea. Therefore, claims 24 and 26 are subject-matter ineligible.
Claim 25 depends directly or indirectly from claim 1. Claim 27 depends directly or indirectly from claim 8. The claims provide more details or specifics to the additional element of “[(h)] generating a validated model learning model,” (claims 25 and 27: “wherein [(h)] the machine-learning model is validated across a plurality of validation datasets, [(h.1)] each of the plurality of validation datasets corresponding to a different user population or domain), and accordingly, is merely more specific to the additional element.
Also, the claims provide more details or specifics to the additional element of [(g)] iteratively tuning,” (claims 25 and 27: “wherein . . . [(g)] the iterative tuning is configured [(g)(e)] to adjust the hyperparameters until the machine-learning model satisfies dataset-specific constraints or target values for each of the plurality of validation datasets”), and accordingly, is merely more specific to the additional element. Therefore, claims 25 and 27 are subject-matter ineligible.
Response to Arguments
6. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Claim Rejections - 35 U.S.C. § 101
7. Applicant submits that “The disclosed improvement is a hyperparameter-tuning technique that performs multi-objective optimization while enforcing deployment constraints, thereby producing a single validated model that is accurate, stable, and deployment-ready, solving the problem that "[s]tandard hyperparameter tuning algorithms perform the tuning operation of hyperparameters with a single goal (e.g., model accuracy) under consideration" and thereby "ignore[] other important goals (e.g., regression errors)." Independent claim 1 recites this improvement concretely and in detail, not at the level of a result.
Specifically, claim 1 requires:
(i) selecting a plurality of metrics for a multi-objective optimization, the plurality being required to include a regression error plus at least one of model size, training time, accuracy, stability, or confidence score, each metric associated with a target score, a penalty factor, and a bonus factor;
(ii) formulating a weighted loss function using second weights "in an asymmetric loss technique," in which "the penalty factor is used as a higher value for the second weight associated with the metric failing the target value and the bonus factor is used as a lower value for the second weight associated with the metric exceeding the target value";
(iii) establishing one or more constraints "at least one from among a maximum model size, a maximum inference latency, and a maximum training time"; and
(iv) iteratively tuning the hyperparameters through a specific closed loop: (a) selecting initial values, (b) evaluating the loss function, (c) determining whether the loss function is optimized and whether each established constraint is satisfied, and ( d) if not, searching for new values, reconfiguring the model, and repeating until "the weighted loss function being optimized and each constraint being satisfied," at which point a validated model is generated.
These are not result-oriented recitations; they are the specific operations that constitute the improvement. The claim therefore "reflects the disclosed improvement" as required by the second part of the MPEP § 2106.04(d)(l) analysis the Examiner invokes.” (Response at pp. 2-3).
Examiner Response:
Examiner respectfully disagrees. Under Step 2A Prong Two, the rejection identifies any additional elements (specifically point to claim features/limitations/steps) recited in the claim beyond the identified judicial exception; and evaluates 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). Moreover, all of claimed additional elements are given weight, even when these elements represent well-understood, routine, conventional activity. (see MPEP § 2106.07(a)).
Generally, under Alice/Mayo Step 1, the determination is whether the claim is directed to an abstract idea, the analysis progresses to additional elements of the claims, with respect to Step 2A Prong Two (“integration”), and Step 2B (“significantly more”).
As set out above in detail, the additional elements recited in the claim beyond the identified abstract idea include “a machine learning model,” “a digital assistant builder platform,” and “a digital assistant chatbot system (DACS),” and a “network,” which are recited at a high-level of generality, and accordingly, are generic computer components in which the abstract ideas are applied, (MPEP § 2106.05(f)), and does not integrate the abstract idea into a practical application. The guidance explains, with regard to Desjardins, that the “specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system.” (MPEP § 2106.04(d)(1) (second & last paragraphs)). In the Desjardins claims, the Board deemed the limitations to “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.” (MPEP § 2106.04(d) sub III).
Generally, the disclosure describes a way to tune a chatbot’s machine-learning model using more than one goal at the same time. Instead of optimizing only one measure, like accuracy, the system can consider several metrics such as regression error, model size, training time, stability, and confidence score. Each metric gets its own target score, penalty factor, and bonus factor. The system also gives each metric a weight so some goals matter more than others. That is, the disclosure relates to a need for multi-objective tuning with configurable priorities, dataset weighting, and constraints (see, e.g., Specification ¶¶ 0029 & 0070-73).
The claims do recite “selecting, by a digital assistant builder platform, in a multi-objective optimization, a plurality of metrics for which hyperparameters of a machine-learning model are to be tuned.” However, the claims do not reflect an improvement to the additional elements providing for a multi-objective optimization, simply that it is a matter of metric selection having weighted preferences.
In this respect, the Examiner response further explains the considerations and opinions to Applicant’s claims in view of the subject-matter eligibility analysis. (Non-Final Action dated 09 April 2026 at pp. 10-14).
Accordingly, in view thereof, the claims are subject-matter ineligible, as set out above in detail.
Conclusion
8. THIS ACTION IS MADE FINAL. 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.
9. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20200143243 to Liang et al.) teaches a visualization of the AutoML implementation system 10 described herein and referred to as LEAF (Learning Evolutionary AI Framework). As described further below, LEAF leverages and extends the existing state-of-the-art evolutionary algorithm for architecture search called CoDeepNEAT (CDN), which evolves both hyperparameters and network structure. While LEAF's hyperparameter optimization ability already matches those of other AutoML systems, LEAF's additional ability to optimize DNN architectures makes it possible to achieve state-of-the-art results. The speciation and complexification heuristics inside CDN also allow for it to be adapted to multiobjective optimization to find minimal architectures.
(Riquelme et al., “Performance metrics in multi-objective optimization,” IEEE (2015)) teaches 54 multi-objective-optimization metrics in the specialized literature, discussing the usage, tendency and advantages/disadvantages of the most cited ones in order to give researchers enough information when choosing metrics is necessary. The review process performed in this work indicates that the hypervolume is the most used metric, followed by the generational distance, the epsilon indicator and the inverted generational distance.
10. 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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, 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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