DETAILED ACTION 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. The following actions are in response to the original filing of 08/14/2023. Claims 1-20 are pending and have been considered below. Claim Objections Claim 1 is objected to because of the following informalities: line 14: “d the raw dataset”. The letter “d” appears to be a typographical error. Appropriate correction is required. Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer . Claims 1, 9, 11-12 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4 and 5 of copending Application No. 18/506,017 (‘017) . Although the claims at issue are not identical, they are not patentably distinct from each other (instant claims 1, 12 and 20 by claim 1 of ‘017; instant claim 9 by claim 4 of ‘017; instant claim 11 by claim 5 of ‘017) . This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. 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 abstract ideas without significantly more. Regarding claims 1, 12 and 20: Step 1, MPEP 2106.03: These limitations have been determined, under Step 1, to be statutory categories of invention: A method [..] (claim 1) A system comprising at least one computer including a processor and a memory [..] (claim 12) A non-transitory computer readable storage medium, including instructions stored thereon [..] (claim 20) Step 2A Prong One MPEP 2106.04, 2106.04(a): These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations , MPEP 2106.04(a)(2)(I) : [..] and calculates, for each attribute of a plurality of annotated sensitive attributes S, a sensitive attribute suppression loss [..] [..] and calculates, for each attribute of a plurality of annotated useful attributes U, an annotated useful attribute preservation loss [..] [..] and calculates, for an unannotated useful attribute F, an unannotated useful attribute preservation loss [..] [..] combining the sensitive attribute suppression loss, the annotated useful attribute preservation loss, and the unannotated useful attribute preservation loss into a total loss [..] Step 2A Prong Two, MPEP 2106.04(d): These limitations represent , under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools , MPEP 2106.05 (f): [..] a machine learning model on at least one computer comprising a processor and a memory [..] [..] a sensitive attribute suppression module of the machine learning model [..] [..] an annotated useful attribute preservation module of the machine learning model [..] [..] an unannotated useful attribute preservation module of the machine learning model [..] [..] a neural network θ [..] [..] a neural network ϕ′ [..] [..] a neural network ψ [..] [..] a neural network ψ′ [..] [..] a neural network η [..] These limitations represent, under Step 2A Prong Two, mere instructions , mere instructions to implement the abstract idea at a high level of generality , MPEP 2106.05: [..] executing a machine learning model [..] [..] wherein the data transformation module accepts a raw dataset as input to a neural network θ, and wherein the neural network θ outputs a transformed dataset [..] [..] wherein the sensitive attribute suppression module accepts the raw dataset as input to a neural network ϕ, accepts the transformed dataset as input to a neural network ϕ′ [..] [..] wherein the annotated useful attribute preservation module accepts d the raw dataset as input to a neural network ψ, accepts the transformed dataset as input to a neural network ψ′ [..] [..] wherein the unannotated useful attribute preservation module accepts the transformed dataset and the raw dataset as input to a neural network η [..] [..] training the neural network θ and the neural network η using the total loss [..] These limitations represent , under Step 2A Prong Two, mere data gathering, MPEP 2106.05 : [..] providing a data transformation module [..] [..] providing a sensitive attribute suppression module [..] [..] providing an annotated useful attribute preservation module [..] [..] providing an unannotated useful attribute preservation module [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality , MPEP 2106.05( d ) : [..] a machine learning model on at least one computer comprising a processor and a memory [..] [..] a sensitive attribute suppression module of the machine learning model [..] [..] an annotated useful attribute preservation module of the machine learning model [..] [..] an unannotated useful attribute preservation module of the machine learning model [..] [..] a neural network θ [..] [..] a neural network ϕ′ [..] [..] a neural network ψ [..] [..] a neural network ψ′ [..] [..] a neural network η [..] These limitations are considered, under Step 2B, mere instructions to “ apply it” , MPEP 2106.05(f): [..] executing a machine learning model [..] [..] wherein the data transformation module accepts a raw dataset as input to a neural network θ, and wherein the neural network θ outputs a transformed dataset [..] [..] wherein the sensitive attribute suppression module accepts the raw dataset as input to a neural network ϕ, accepts the transformed dataset as input to a neural network ϕ′ [..] [..] wherein the annotated useful attribute preservation module accepts d the raw dataset as input to a neural network ψ, accepts the transformed dataset as input to a neural network ψ′ [..] [..] wherein the unannotated useful attribute preservation module accepts the transformed dataset and the raw dataset as input to a neural network η [..] [..] training the neural network θ and the neural network η using the total loss [..] These limitations are considered, under Step 2B, insignificant extra-solution activity of data gathering/selecting a particular type of data, MPEP 2106.05(g) : [..] providing a data transformation module [..] [..] providing a sensitive attribute suppression module [..] [..] providing an annotated useful attribute preservation module [..] [..] providing an unannotated useful attribute preservation module [..] Regarding claims 2 and 13 : Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea at a high level of generality, MPEP 2106.05: [..] wherein the neural network ϕ is trained prior to the training of the neural network θ and the neural network η using the total loss [..] [..] wherein the neural network ϕ is trained using a traditional supervised learning method [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein the neural network ϕ is trained prior to the training of the neural network θ and the neural network η using the total loss [..] [..] wherein the neural network ϕ is trained using a traditional supervised learning method [..] Regarding claims 3 and 14 : Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent , under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools , MPEP 2106.05 (f): [..] wherein neural network ϕ is fixed during the training of the neural network θ and the neural network η using the total loss [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein neural network ϕ is fixed during the training of the neural network θ and the neural network η using the total loss [..] Regarding claims 4 and 15 : Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent , under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools , MPEP 2106.05 (f): [..] wherein the neural network ϕ′ is trained using a traditional supervised learning method at a same time as the training of the neural network θ and the neural network η using the total loss [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein the neural network ϕ′ is trained using a traditional supervised learning method at a same time as the training of the neural network θ and the neural network η using the total loss [..] Regarding claims 5 and 16: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea at a high level of generality, MPEP 2106.05: [..] wherein the neural network ψ is trained prior to the training of the neural network θ and the neural network η using the total loss [..] [..] wherein the neural network ψ is trained using a traditional supervised learning method [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein the neural network ψ is trained prior to the training of the neural network θ and the neural network η using the total loss [..] [..] wherein the neural network ψ is trained using a traditional supervised learning method [..] Regarding claims 6 and 17: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent , under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools , MPEP 2106.05 (f): [..] wherein neural network ψ is fixed during the training of the neural network θ and the neural network η using the total loss [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein neural network ψ is fixed during the training of the neural network θ and the neural network η using the total loss [..] Regarding claims 7 and 18: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. Step 2A Prong Two, MPEP 2106.04(d): These limitations represent , under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools , MPEP 2106.05 (f): [..] wherein the neural network ψ′ is trained using a traditional supervised learning method at a same time as the training of the neural network θ and the neural network η using the total loss [..] Step 2 B , MPEP 2106.05 : These limitations are considered, under Step 2B, mere instructions to “apply it”, MPEP 2106.05(f): [..] wherein the neural network ψ′ is trained using a traditional supervised learning method at a same time as the training of the neural network θ and the neural network η using the total loss [..] Regarding claims 8 and 19: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations , MPEP 2106.04(a)(2)(I) : [..] wherein the unannotated useful attribute preservation loss is an InfoNCE contrastive learning loss [..] Step 2A Prong Two, MPEP 2106.04(d): All limitations are part of the abstract idea. Step 2 B , MPEP 2106.05 : All limitations are part of the abstract idea. Regarding claim 9: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations , MPEP 2106.04(a)(2)(I) : [..] wherein the sensitive attribute suppression loss is a constraint to an estimation of mutual information between each attribute of the plurality of annotated sensitive attributes S and the transformed dataset [..] Step 2A Prong Two, MPEP 2106.04(d): All limitations are part of the abstract idea. Step 2 B , MPEP 2106.05 : All limitations are part of the abstract idea. Regarding claim 10: Step 1, MPEP 2106.03: Analysis of respective parent is incorporated. Step 2A Prong One MPEP 2106.04, 2106.04(a): Analysis of respective parent is incorporated. These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations , MPEP 2106.04(a)(2)(I) : [..] wherein the annotated useful attribute preservation loss is a constraint to an estimation of mutual information between each attribute of a plurality of annotated useful attributes U and the transformed dataset [..] Step 2A Prong Two, MPEP 2106.04(d): All limitations are part of the abstract idea. Step 2 B , MPEP 2106.05 : All limitations are part of the abstract idea. Allowable Subject Matter Claims 1, 12 and 20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. Claims 2-11 and 13-19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is an examiner’s statement of reasons for allowance: Each of independent claims 1, 12 and 20 claim inventions that encompass training a machine learning model for sensitive and useful attribute suppression and preservation. The machine learning model has modules using neural networks for each of data transformation, attribute suppression, annotated and unannotated attribute preservation. Initially, the data transformation module uses a first neural network to transform a raw dataset. The attribute suppression module accepts both the untransformed dataset and transformed dataset in second and third neural networks and calculates , for each attribute of an annotated suppression attribute dataset input data , a suppression loss function. The annotated useful attribute module similarly accepts both the untransformed dataset and transformed dataset in fourth and fifth and calculates, for each attribute of an annotated useful attribute dataset input data, an annotated useful attribute loss function. The unannotated attribute preservation module also accepts both the untransformed dataset and transformed dataset in a sixth neural network and calculates, for an unannotated useful attribute, an unannotated useful attribute loss function. Finally, the three loss functions are combined into a total loss function that is use to train the first and sixth neural networks of the machine learning model. While the prior generally shows privacy preservation using machine learning models having modules with combined loss functions (Gonzalez Sanchez et al., US 2024/0119289 A1 , Dave et al . "Spact: Self-supervised privacy preservation for action recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2022 ) , the particular combination of modules and neural networks, raw and transformed data inputs, and combined loss functions of the present invention is not taught or suggested by prior art . Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sakhinana ; Sagar Srinivas et al., US 20230281427 A1, PRIVACY PRESERVING GENERATIVE MECHANISM FOR INDUSTRIAL TIME-SERIES DATA DISCLOSURE Cao; Tianshi et al., US 20220108213 A1, DIFFERENTIAL PRIVACY DATASET GENERATION USING GENERATIVE MODELS Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ANDREW L TANK whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1692 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Thursday 9a-6p . 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. 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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. /ANDREW L TANK/ Primary Examiner, Art Unit 2141