CTFR 18/012,752 CTFR 84315 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION This action is response to the communication filed on February 10, 2026. Claims 1-11 are pending. Response to Arguments Applicant’s arguments regarding art rejection filed on February 10, 2026 have been considered but are moot in the view of new ground of rejection. Applicant argument regarding 101 rejection are not persuasive. Regarding 101 rejection applicant argues the limitation “wherein the neural network models are trained based on same supervised learning data, and wherein the learning of two of the neural network models enhances robustness of the neural network models against adversarial examples” relate to practical application that overcomes a technical problem in the field of artificial intelligence. In response examiner respectfully disagree. Human brain can be interpreted as neural network that can be trained based data to identify efficient way to perform the neural network action as claimed. Hence, limitation is a mental process. Further, it appears to examiner that applicant refers to specification paragraph [0002], [0021]-[0025], [0040], and [0073] to show the improvement of the technology. However, the citation of the specification appears to nothing but clearly mathematical algorithm which clearly human brain can solve it. If necessary, user can use physical aid such as pen and paper (See MPEP 2106.04(a)(2) III, B). Hence, limitation is a mental process and alternatively it also falls under mathematical concept. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites a memory configured to store instructions; and a processor configured to execute the instructions to: obtain incorrect answer class prediction probability vectors by excluding a correct answer class element from prediction probability vectors of neural network models for supervised learning data; and perform learning of two of the neural network models, the learning of the two of the neural network models reducing a value of an objective function which includes a diversity function, a value of diversity function decreasing as an angle between the incorrect answer class prediction probability vectors of the two neural network models increases, wherein the neural network models are trained based on same supervised learning data, and wherein the learning of two of the neural network models enhances robustness of the neural network models against adversarial examples. The claim recited the limitation of “ perform learning of two of the neural network models so as to further reduce a value of an objective function which includes a diversity function, a value of diversity function decreasing as an angle between the incorrect answer class prediction probability vectors of the two neural network models increases, wherein the neural network models are trained based on same supervised learning data, and wherein the learning of two of the neural network models enhances robustness of the neural network models against adversarial examples” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User brain can function as neural network. Hence, user can mentally perform the function as claimed. Therefore, the limitation is a mental process. The claim recites one additional element: obtain incorrect answer class prediction probability vectors by excluding a correct answer class element from prediction probability vectors of neural network models for supervised learning data. The obtaining step as recited amounts to mere data gathering for use in the detection step, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Accordingly, even in combination, 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 is directed to the abstract idea. The claim does 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 element of obtaining step amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra- solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. (Note that the claims also alternatively falls under mathematical concept). Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the diversity function includes computation of an evaluation value of a magnitude of an angle between the incorrect answer class prediction probability vectors, for all combinations of two of the neural network models among all of the neural network models that are a learning target, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the diversity function includes, as computation of an evaluation value of a magnitude of an angle between two of the incorrect answer class prediction probability vectors, calculation of cosine similarity of the two incorrect answer class prediction probability vectors, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the diversity function includes computation to calculate an average of cosine similarities of the incorrect answer class prediction probability vectors of two of the neural network models, for all combinations of two of the neural network models among all of the neural network models that are a learning target, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. As to claims 5-6 , they have similar limitations as of claim 1 above. Hence, they are rejected under the same rational as of claim 1 above. Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the neural network models are trained to reduce similarity between the two of the neural network models, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the processor is further configured to perform the learning by calculating the objective function with a computational complexity of O(Lm2), where L is the number of output vector classes and m is the number ofneural network models, thereby substantially reducing computational complexity for diversity promotions as compared to conventional methods having a computational complexity of O(Lm2+m3), which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein obtaining incorrect answer class prediction probability vectors performs a technical data transformation process for extracting deep misclassification tendency information of each neural network model, which is distinct from generic data acquisition, thereby enabling effective diversity promotion, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the diversity function includes calculation of cosine similarity of the two incorrect answer class prediction probability vectors, thereby evaluating diversity based on directional tendencies of misclassification regardless of prediction magnitude, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim 11 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of an incorrect answer prediction. The claim recites the limitations of wherein the diversity function includes computation to calculate an average of cosine similarities of the incorrect answer class prediction probability vectors of two of the neural network models, for all combinations of two of the neural network models that are a learning target, thereby preventing the value of the diversity function from increasing or decreasing depending on the number of neural network models and ensuring a consistent degree of influence of the diversity function on the objective function during learning, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo et al. (Pub. No. : US 20200151613 A1) in the view of Park et al. (Pub. No. : US 20210166071 A1) and Pappas et al. (Pub. No. : US 20220101627 A1 ) As to claim 1 Yoo teaches a learning device comprising: a memory configured to store instructions and a processor configured to execute the instructions to: obtain incorrect answer class prediction probability by excluding a correct answer class element from prediction probability of neural network models for supervised learning data ( paragraph [0154]: prediction incorrect answer set 225 is selected by using the miss-prediction probability calculated by the calculation model 224); and perform learning of two of the neural network models, the learning of the two of the neural network models reducing a value of an objective function ( paragraphs [0018], [0085]: calculating the miss-prediction probability of the first model may include: constructing a second model for calculating the miss-prediction probability of the first model based on an evaluation result of the first model, where the model is a neural network-based model); wherein the neural network models are trained based on same supervised learning data (paragraph [0003], [0005]: As shown in FIG. 1, supervised learning is a machine learning method to construct a target model 3 for performing a target task by learning a dataset 2) Yoo does not explicitly disclose but Park teaches prediction probability vectors ( paragraph [0064]: neural network to predict a correct answer, wherein relatively small feature angle may indicate a relatively high similarity between a feature vector of an input image and the first class vector of the first class that is a correct answer class, and may indicate that a class to which the input image belongs is correctly predicted) which includes a diversity function, a value of diversity function decreasing as an angle between the incorrect answer class prediction probability vectors of the two neural network models increases ( paragraph [0062]: In Equation 1,e.sup.s(cos(aθ.sub.y.sub.i.sup.=b)−c) corresponds to a correct answer term representing the difference between the feature vector and the first class vector, and τ.sub.j=1,j≠y.sub.i.sup.n e.sup.s cos θ.sup.j corresponds to an incorrect answer term representing a difference between the feature vector and another class vector). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Yoo by adding above limitation as taught by Park to improve the accuracy of the trained neural network over typical trained neural networks (Park, paragraph [0065]). Yoo and Park do not explicitly disclose but Pappas teaches wherein the learning of two of the neural network models enhances robustness of the neural network models against adversarial examples ( Paragraph [0016], [0022], [0089]: While adversarial training provides robustness against the imperceptible perturbations described in FIG. 1A). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Yoo and Park by adding above limitation as taught by Pappas to improving the robustness of deep learning (Pappas, paragraph [0041]). As to claim 2 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the diversity function includes computation of an evaluation value of a magnitude of an angle between the incorrect answer class prediction probability vectors, for all combinations of two of the neural network models among all of the neural network models that are a learning target ( paragraphs [0056], [0062] ). As to claim 3 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the diversity function includes, as computation of an evaluation value of a magnitude of an angle between two of the incorrect answer class prediction probability vectors, calculation of cosine similarity of the two incorrect answer class prediction probability vectors ( paragraph [0094] ). As to claim 4 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the diversity function includes computation to calculate an average of cosine similarities of the incorrect answer class prediction probability vectors of two of the neural network models, for all combinations of two of the neural network models among all of the neural network models that are a learning target ( paragraphs [0062], [0094] ). As to claims 5-6 , they have similar limitations as of claim 1 above. Hence, they are rejected under the same rational as of claim 1 above. As to claim 7 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the neural network models are trained to reduce similarity between the two of the neural network models ( paragraphs [0018], [0085] ). As to claim 8 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the processor is further configured to perform the learning by calculating the objective function with a computational complexity of O(Lm2), where L is the number of output vector classes and m is the number ofneural network models, thereby substantially reducing computational complexity for diversity promotions as compared to conventional methods having a computational complexity of O(Lm2+m3) ( paragraph [0062]-[0064] ). As to claim 9 Yoo together with Park and Pappas teaches a learning device according to claim 1. Yoo teaches wherein obtaining incorrect answer class prediction probability vectors performs a technical data transformation process for extracting deep misclassification tendency information of each neural network model, which is distinct from generic data acquisition, thereby enabling effective diversity promotion ( paragraph [0122] ). As to claim 10 Yoo together with Park and Pappas teaches a learning device according to claim 1. Yoo teaches wherein the diversity function includes calculation of cosine similarity of the two incorrect answer class prediction probability vectors, thereby evaluating diversity based on directional tendencies of misclassification regardless of prediction magnitude ( paragraph [0154] ). As to claim 11 Yoo together with Park and Pappas teaches a learning device according to claim 1. Park teaches wherein the diversity function includes computation to calculate an average of cosine similarities of the incorrect answer class prediction probability vectors of two of the neural network models, for all combinations of two of the neural network models that are a learning target, thereby preventing the value of the diversity function from increasing or decreasing depending on the number of neural network models and ensuring a consistent degree of influence of the diversity function on the objective function during learning ( paragraph [0094] ). Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context. Conclusion 07-40 AIA 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. The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MD I UDDIN whose telephone number is (571)270-3559. The examiner can normally be reached M-F, 8:00 am to 5:00 pm. 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, Sherief Badawi can be reached at 571-272-9782. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MD I UDDIN/Primary Examiner, Art Unit 2169 Application/Control Number: 18/012,752 Page 2 Art Unit: 2169 Application/Control Number: 18/012,752 Page 3 Art Unit: 2169 Application/Control Number: 18/012,752 Page 4 Art Unit: 2169 Application/Control Number: 18/012,752 Page 5 Art Unit: 2169 Application/Control Number: 18/012,752 Page 6 Art Unit: 2169 Application/Control Number: 18/012,752 Page 7 Art Unit: 2169 Application/Control Number: 18/012,752 Page 8 Art Unit: 2169 Application/Control Number: 18/012,752 Page 9 Art Unit: 2169 Application/Control Number: 18/012,752 Page 10 Art Unit: 2169 Application/Control Number: 18/012,752 Page 11 Art Unit: 2169 Application/Control Number: 18/012,752 Page 12 Art Unit: 2169 Application/Control Number: 18/012,752 Page 13 Art Unit: 2169
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