CTNF 18/140,130 CTNF 94006 DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the Application filed on 04/27/2023. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are independent claims. Claim Rejections - 35 U.S.C. § 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-7 are directed towards the statutory category of a process. Claims 8-14 are directed towards the statutory category of an article of manufacture. Claims 15-20 are directed towards the statutory category of a machine. With respect to claim 1: 2A Prong 1 : This claim is directed to a judicial exception. A… method for imputing missing data in the presence of data quality disparity, the method comprising (mental process): formulating an optimization problem of imputing missing values in a dataset as a black- box optimization problem with an objective of jointly maximizing both a fairness metric and an accuracy of a model (mental process); and identifying missing values to be imputed in said dataset based on maximizing said fairness metric and said accuracy of said model (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: computer-implemented (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: computer-implemented (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)). With respect to claim 2: 2A Prong 1 : This claim is directed to a judicial exception. imputing missing values in data samples of said dataset for each sensitive group in said dataset separately (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 3: 2A Prong 1 : This claim is directed to a judicial exception. 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: training said model using sample weights corresponding to said data samples with said imputed missing values jointly weighed based on data quality and data bias (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: training said model using sample weights corresponding to said data samples with said imputed missing values jointly weighed based on data quality and data bias (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). With respect to claim 4: 2A Prong 1 : This claim is directed to a judicial exception. said sample weights are used to weigh terms in a loss function of said model (mental process and/or mathematical concept). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 5: 2A Prong 1 : This claim is directed to a judicial exception. selecting one of a plurality of imputation algorithms to identify said missing values to be imputed in said dataset which maximizes said fairness metric and said accuracy of said model (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 6: 2A Prong 1 : This claim is directed to a judicial exception. solving said optimization problem using a black-box optimization technique to identify said missing values to be imputed in said dataset which maximizes said fairness metric and said accuracy of said model (mental process and/or mathematical concept). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 7: 2A Prong 1 : This claim is directed to a judicial exception. 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: said black-box optimization technique comprises reinforcement learning (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The remaining claims 8-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claims 1-7 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more. Claim Rejections - 35 U.S.C. § 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 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 of this title, 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1, 2, 5, 6, 8, 9, 12, 13, 15, 16, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang et al. (Zhang, Yiliang, and Qi Long. "Fairness-aware missing data imputation." In Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022 . 2022, hereinafter Zhang) in view of Perrone et al. (Perrone, Valerio, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, and Cédric Archambeau. "Fair bayesian optimization." In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pp. 854-863. 2021, hereinafter Perrone) . As to independent claims 1, 8, and 15 , Zhang teaches: A computer-implemented method for imputing missing data in the presence of data quality disparity, the method comprising ( Page 1, "imputation model" ): formulating an optimization problem of imputing missing values in a dataset… both a fairness metric and an accuracy of a model ( Page 2, "accurate imputation while guaranteeing imputation fairness." Page 4, "the second term forces to produce accurate imputations, the third term regularizes the model to have small imputation fairness risk" ); and identifying missing values to be imputed in said dataset based… said fairness metric and said accuracy of said model ( Page 1, "Missing values frequently occur in real-world datasets". Page 2, "accurate imputation while guaranteeing imputation fairness." Page 4, "the second term forces to produce accurate imputations, the third term regularizes the model to have small imputation fairness risk" ). Zhang does not appear to expressly teach as a black-box optimization problem with an objective of jointly maximizing; and based on maximizing. Perrone teaches as a black-box optimization problem with an objective of jointly maximizing ( Page 855, "treating the pipeline as an opaque box whose internals cannot be modified". Page 857, Algorithm 1, line 11, "return Best fair hyperparameter configuration in C" ); and based on maximizing ( Page 857, Algorithm 1, line 11, "return Best fair hyperparameter configuration in C" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the fairness-aware missing data imputation of Zhang to include the fair optimization techniques of Perrone to optimize the performance of any ML model while enforcing one or multiple fairness constraints (see Perrone at abstract). As to dependent claims 2, 9, and 16 , Zhang further teaches imputing missing values in data samples of said dataset for each sensitive group in said dataset separately ( Page 3, "imputations for samples from different groups defined by a sensitive attribute" ). As to dependent claims 5, 12, and 19 , Zhang further teaches selecting one of a plurality of imputation algorithms to identify said missing values to be imputed in said dataset which maximizes said fairness metric and said accuracy of said model ( Page 6, Table 2, compares different imputation models accuracy and fairness metrics ). As to dependent claims 6, 13, and 20 , Zhang further teaches said fairness metric and said accuracy ( Page 2, "accurate imputation while guaranteeing imputation fairness." Page 4, "the second term forces to produce accurate imputations, the third term regularizes the model to have small imputation fairness risk" ). Perrone further teaches solving said optimization problem using a black-box optimization technique to identify said missing values to be imputed in said dataset which maximizes… said model ( Page 855, "treating the pipeline as an opaque box whose internals cannot be modified". Page 857, Algorithm 1, line 11, "return Best fair hyperparameter configuration in C" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the fairness-aware missing data imputation of Zhang to include the fair optimization techniques of Perrone to optimize the performance of any ML model while enforcing one or multiple fairness constraints (see Perrone at abstract) . 07-21-aia AIA Claim s 3, 4, 10, 11, 17, and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang in view of Perrone and Wang et al. (Wang, Yanchen, and Lisa Singh. "Analyzing the impact of missing values and selection bias on fairness." International Journal of Data Science and Analytics 12, no. 2 (2021): 101-119, hereinafter Wang) . As to dependent claims 3, 10, and 17 , the respective rejections of claims 2, 9, and 16 are incorporated. Zhang teaches jointly weighed based on data quality and data bias ( Page 2, "accurate imputation while guaranteeing imputation fairness." Page 4, "the second term forces to produce accurate imputations, the third term regularizes the model to have small imputation fairness risk" ). Zhang does not appear to expressly teach training said model using sample weights corresponding to said data samples with said imputed missing values. Wang teaches training said model using sample weights corresponding to said data samples with said imputed missing values ( Abstract, "reweighting and resampling based upon the missing value generation process" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the fairness-aware missing data imputation of Zhang to include the fairness techniques of Wang to improve fairness (see Wang at page 102). As to dependent claim 4, 11, and 18 , Wang further teaches said sample weights are used to weigh terms in a loss function of said model ( Abstract, "reweighting and resampling based upon the missing value generation process" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the fairness-aware missing data imputation of Zhang to include the fairness techniques of Wang to improve fairness (see Wang at page 102) . 07-21-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang in view of Perrone and Chaybouti et al. (Chaybouti, Sofian, Ludovic Dos Santos, Cedric Malherbe, and Aladin Virmaux. "Meta-learning of black-box solvers using deep reinforcement learning." In Sixth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems . 2022, hereinafter Chaybouti) . As to dependent claims 7 and 14 , the respective rejections of claims 6 and 13 are incorporated. Zhang does not appear to expressly teach said black-box optimization technique comprises reinforcement learning. Chaybouti teaches said black-box optimization technique comprises reinforcement learning ( Title, "Black-box Solvers Using Deep Reinforcement Learning" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the fairness-aware missing data imputation of Zhang to include the black-box techniques of Chaybouti to optimize a specific class of functions (see Chaybouti at page 1). Citation of Pertinent Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liu et al. (Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander Gray. "An ADMM based framework for automl pipeline configuration." In Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, pp. 4892-4899. 2020.) teaches an ADMM based framework for autoML pipeline configuration . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action . It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /Casey R. Garner/Primary Examiner, Art Unit 2123 Application/Control Number: 18/140,130 Page 2 Art Unit: 2123 Application/Control Number: 18/140,130 Page 3 Art Unit: 2123 Application/Control Number: 18/140,130 Page 4 Art Unit: 2123 Application/Control Number: 18/140,130 Page 5 Art Unit: 2123 Application/Control Number: 18/140,130 Page 6 Art Unit: 2123 Application/Control Number: 18/140,130 Page 7 Art Unit: 2123 Application/Control Number: 18/140,130 Page 8 Art Unit: 2123 Application/Control Number: 18/140,130 Page 9 Art Unit: 2123 Application/Control Number: 18/140,130 Page 10 Art Unit: 2123 Application/Control Number: 18/140,130 Page 11 Art Unit: 2123 Application/Control Number: 18/140,130 Page 12 Art Unit: 2123 Application/Control Number: 18/140,130 Page 13 Art Unit: 2123
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