DETAILED ACTION
This action is in response to the claims filed 01/10/2024 for Application number 18/408,599. Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
validating a plurality of candidate models based on a plurality of first adversarial validation data to generate a first accuracy corresponding to each of the candidate models, wherein the first adversarial validation data is generated by a first adversarial attack adjustment performed on the validation data based on an initial model can be considered to be an evaluation in the human mind,
performing a second adversarial attack adjustment on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data corresponding to each of the candidate models respectively can be considered to be an evaluation in the human mind
validating the candidate models based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models can be considered to be an evaluation in the human mind
selecting at least one output model from the candidate models based on the first accuracy and the second accuracy corresponding to each of the candidate models.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “a processor, coupled to the storage, configured to execute the following operations”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Additionally, the claim recites “a plurality of candidate models”, “an initial model”, and “one output model”. These elements that are recited are only generally linked to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: a storage, configured to store a plurality of training data and a plurality of validation data; This limitation is a mere data gathering step and thus is an insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processor and storage to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally, the additional elements of a plurality of candidate models, an initial model, and one output model are generally linked to the judicial exception.
Furthermore, the limitation of a storage, configured to store a plurality of training data and a plurality of validation data is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(iv), “Storing and retrieving information in memory”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional elements to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: performing the first adversarial attack adjustment on the training data based on the initial model to generate a plurality of adversarial training data; This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
training the initial model based on the training data and the adversarial training data to generate the candidate models. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 2 is further incorporated, and further, the claim recites: training the initial model corresponding to a plurality of parameter sets based on the training data and the adversarial training data to generate the candidate models corresponding to the parameter sets. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 3 is further incorporated, and further, the claim recites: wherein each of the candidate models corresponds to a different one of the parameter sets. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: generating a first noise based on the initial model by using an adversarial attack function and generating the first adversarial validation data based on the validation data and the first noise. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 5 is further incorporated, and further, the claim recites: wherein the first adversarial attack adjustment comprises the following operation: adding the first noise into each of the validation data to adjust the validation data. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 6 is further incorporated, and further, the claim recites: compressing the adjusted validation data to generate the first adversarial validation data. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: generating a second noise based on one of the candidate models by using an adversarial attack function; and generating the second adversarial validation data based on the validation data and the second noise. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: selecting a first candidate model having a highest first accuracy and a second candidate model having a highest second accuracy as the at least one output model from the candidate models. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the initial model is a pre-trained machine learning model. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 11-20, they recite features similar to claims 1-10 and are rejected for at least the same reasons therein.
Allowable Subject Matter
Claims 1-20 are objected to as being allowable over prior art if all outstanding rejections were withdrawn. None of the prior art, either alone or in combination, fairly discloses limitations of claims 1 and 11 in particular:
performing a second adversarial attack adjustment on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data corresponding to each of the candidate models respectively;
validating the candidate models based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models; and
selecting at least one output model from the candidate models based on the first accuracy and the second accuracy corresponding to each of the candidate models.
The closest prior art uncovered was Srisakaokul et al. (“MulDef: Multi-model-based Defense Against Adversarial Examples for Neural Networks”) which discloses using a family of models constructed from a target model to achieve robustness diversity. The reference does further teaching generating a first accuracy corresponding to each candidate model and a second accuracy which is an average accuracy of all the models however the reference does not explicitly teach performing a second adversarial attack adjustment on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data and validating the candidate models based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models and further selecting at least one output model based on the first and second accuracy corresponding to each model.
Tramer et al. (“Ensemble Adversarial Training: Attacks and Defenses”) discloses using adversarial examples with perturbed inputs from other pre-trained models however fails to explicitly teach performing a second adversarial attack adjustment on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data and validating the candidate models based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models and further selecting at least one output model based on the first and second accuracy corresponding to each model.
Chen et al. (“US 20220012572 A1”) discloses selecting a new model based on accuracy on adversarial perturbed data, however fails to explicitly disclose validating a plurality of candidate models based on a plurality of first adversarial validation data to generate a first accuracy corresponding to each of the candidate models, wherein the first adversarial validation data is generated by a first adversarial attack adjustment performed on the validation data based on an initial model and performing a second adversarial attack adjustment on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data and validating the candidate models based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models.
None of the prior art uncovered, alone or in combination, teaches the specific steps of performing a second adversarial attack adjustment on the validation data, validating the candidate models based on the corresponding second adversarial validation data and selecting at least one output model from the candidate models based on the first and second accuracy of the candidate models.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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 at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
Prosecution Insights