CTNF 18/562,545 CTNF 98891 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 11/20/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Allowable Subject Matter 07-43 Claim4, 6, and 7 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim Objections 07-29-01 AIA Claim 10 objected to because of the following informalities: Claim 10 recites in the second limitation "a second variable type VART3" instead of "a second variable type VART2" . Appropriate correction is required. 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 and 13 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Step 1 analysis: Independent Claim 1 recites, in part, a method, therefore falling into the statutory category of process. Independent Claim 13 recites, in part, a control unit of a facility, therefore falling into the statutory category of manufacture. Regarding Claim 1: Step 2A: Prong 1 analysis: Claim 1 recites in part: “processing the input with the provided modeling function to determine the recommended setting”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing data. “wherein the modeling function is a function trained based on a Gaussian process with DCOM˜- N (0,K coreg +σ 2 I) defined by a characterizing covariance matrix K coreg and a corresponding characterizing kernel k SEP coreg ”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical relationship (Gaussian functions). “and setting the given parameter to the recommended setting”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses setting a parameter. Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea. Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “starting with a modeling function and a plurality n with n≥2 of given input variables VARv with v=1, . . . , n a prepared recommender system”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (function variables) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “wherein different input variables VARv correspond to different variable types VARTv, for a particular variable type VARTv a plurality Tv of respective variables VARTv,t is available with t=1, . . . , Tv, for each variable type VARTv only one variable VARTv,t is provided as input variable VARv=VARTv,t”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (function variables) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “starting with a modeling function and a plurality n with n≥2 of given input variables VARv with v=1, . . . , n a prepared recommender system” and “wherein different input variables VARv correspond to different variable types VARTv, for a particular variable type VARTv a plurality Tv of respective variables VARTv,t is available with t=1, . . . , Tv, for each variable type VARTv only one variable VARTv,t is provided as input variable VARv=VARTv,t” is/are directed to particular field(s) of use (function variables) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 2: Step 2A: Prong 1 analysis: Claim 2 recites in part: “wherein the characterizing kernel kSEP coreg is a separable kernel defined by a product kSEP coreg=Πe=1 nke of sub-kernels ke.”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation. Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea. Step 2A: Prong 2 analysis: The claim does not recite any additional elements that integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. Regarding Claim 3: Step 2A: Prong 1 analysis: Claim 3 recites in part: “wherein each sub-kernel ke measures the similarity between two variables VARTe,t1, VARTe,t2 with t1,t2 ∈ [1, . . . , Te] of the same variable type VARTe”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses comparing the similarity of two variables of the same type. Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea. Step 2A: Prong 2 analysis: The claim does not recite any additional elements that integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. Regarding Claim 4: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “wherein each sub-kernel is based on a Radial Basis Function (RBF)”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (sub-kernel functions) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “wherein each sub-kernel is based on a Radial Basis Function (RBF)” is/are directed to particular field(s) of use (sub-kernel functions) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 5: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “further comprising a training to optimize the modeling function”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (optimization) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “further comprising a training to optimize the modeling function” is/are directed to particular field(s) of use (optimization) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 6: Step 2A: Prong 1 analysis: Claim 6 recites in part: “the function is a Gaussian process based decomposition function which is trained on an n-dimensional settings database”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical function. Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea. Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “the settings database contains known and/or assumed settings of the adjustable parameter”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (parameter settings) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “the dimensions DIMd with d=1, . . . , n of the settings database corresponds to the variable types VARTd”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (parameter settings) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “the method further comprises optimizing the decomposition function in a plurality of optimization steps by maximizing a log-likelihood with respect to trainable parameters of the decomposition function”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (optimization) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “the settings database contains known and/or assumed settings of the adjustable parameter” , “the dimensions DIMd with d=1, . . . , n of the settings database corresponds to the variable types VARTd” , and “the method further comprises optimizing the decomposition function in a plurality of optimization steps by maximizing a log-likelihood with respect to trainable parameters of the decomposition function” is/are directed to particular field(s) of use (parameter settings and optimization) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 7: Step 2A: Prong 1 analysis: Claim 6 recites in part: “starting with an initial function DCOMin”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical function. “varying parameters defining the provided decomposition function DCOMini to define an actual decomposition function DCOMact”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical function. “decomposing the settings database by applying the actual decomposition function DCOMact on the settings database, resulting in a latent representation LATd for each variable type VARTd”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical function. “joining the latent representations LATd to generate a reconstructed settings database”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses joining two sets of data. “comparing the reconstructed settings database with the provided settings database”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses comparing settings. Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea. Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “wherein variation of the parameters of the decomposition function from DCOMini to DCOMact aims at minimizing a difference between the settings database and the reconstructed settings database”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (optimization) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “wherein variation of the parameters of the decomposition function from DCOMini to DCOMact aims at minimizing a difference between the settings database and the reconstructed settings database” is/are directed to particular field(s) of use (optimization) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 8: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “a first variable type VART1 corresponds to different operating states of an industrial facility”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (facility operations) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “a second variable type VART2 corresponds to different devices of the facility”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (facility operations) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “in case n≥3 a third variable type VART3 corresponds to different adjustable parameters of the devices”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (facility operations) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “a first variable type VART1 corresponds to different operating states of an industrial facility” , “a second variable type VART2 corresponds to different devices of the facility” , and “in case n≥3 a third variable type VART3 corresponds to different adjustable parameters of the devices” is/are directed to particular field(s) of use (facility operations) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 9: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “the settings database contains known and/or assumed settings for observed and/or assumed combinations of adjustable parameters PA, devices DEV, and operating states OS”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (facility operations) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “the recommendation method RM provides, upon receipt of input variables VAR1=OS1, VAR2=DEV1, VAR3=PA1, a recommended setting S for a given adjustable parameter PA1 for a given device DEV1 for a given operating state OS1 of the facility”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (facility operations) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “the settings database contains known and/or assumed settings for observed and/or assumed combinations of adjustable parameters PA, devices DEV, and operating states OS” and “the recommendation method RM provides, upon receipt of input variables VAR1=OS1, VAR2=DEV1, VAR3=PA1, a recommended setting S for a given adjustable parameter PA1 for a given device DEV1 for a given operating state OS1 of the facility” is/are directed to particular field(s) of use (facility operations) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 10: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “a first variable type VART1 corresponds to different customers of an industrial product provider”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (product information) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “a second variable type VART3 corresponds to different products of the industrial product provider”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (product information) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “in case n≥3 a third variable type VART3 corresponds to different purchase features for purchasing the products”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (product information) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “a first variable type VART1 corresponds to different customers of an industrial product provider” , “a second variable type VART3 corresponds to different products of the industrial product provider” , and “in case n≥3 a third variable type VART3 corresponds to different purchase features for purchasing the products” is/are directed to particular field(s) of use (product information) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 11: Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “the settings database contains known and/or assumed settings for observed and/or assumed combinations of customers, products, and purchase features”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (product information) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). “the recommendation method provides, upon receipt of input variables VAR1=CST1, VAR2=PRD1, VAR3=PCF1, a recommended setting for a given purchase feature PCF1 for a given product PRD1 for a given customer CST1”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (product information) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional element(s) of “the settings database contains known and/or assumed settings for observed and/or assumed combinations of customers, products, and purchase features” and “the recommendation method provides, upon receipt of input variables VAR1=CST1, VAR2=PRD1, VAR3=PCF1, a recommended setting for a given purchase feature PCF1 for a given product PRD1 for a given customer CST1” is/are directed to particular field(s) of use (facility operations) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible. Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding Claim 13: Due to claim language similar to that of Claim 1, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below. Step 2A: Prong 2 analysis: The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of: “a controller to control settings of adjustable parameters of devices of the facility, wherein the respective setting of a particular device depends on an actual operating state of the facility”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (controller) (See MPEP 2106.05(f)). Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element(s) of “a controller to control settings of adjustable parameters of devices of the facility, wherein the respective setting of a particular device depends on an actual operating state of the facility” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (controller) (See MPEP 2106.05(f)). Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-3 and 5 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ghazanfar et al (Ghazanfar, M. A., Prügel-Bennett, A., & Szedmak, S. (2012). Kernel-Mapping Recommender system algorithms. Information Sciences, 208, 81–104. doi:10.1016/j.ins.2012.04.012, hereinafter Ghazanfar) . Regarding Claim 1: Ghazanfar teaches A method for providing a setting for a given parameter to be adjusted, the method comprising: starting with a modeling function and a plurality n with n≥2 of given input variables VAR V with v=1, . . . , n a prepared recommender system ( Ghazanfar [Page 83, Section 3, par. 2]: “ In this section we describe an item-based recommender. In the next section we show how we can adapt the approach to a user-based recommender. To perform the recommendation task we consider building the additive and multiplicative models for the residual ratings ”; [Page 83, Section 3.1, par. 1]: “ We use a technique developed by Szedmak and co-workers for learning structured data [54]. In the following we outline how this approach is adapted for solving the collaborative filtering problem. We assume that we have some information about the items which we denote by qi. This may, for example, be the set of ratings riu for u 2Di, or it could be text describing the item i. ”; (EN): the input variable q i of Ghazanfar is analogous to the input variables VART V of the instant application); wherein different input variables VARv correspond to different variable types VARTv, ( Ghazanfar [Page 83, Section 3.1, par. 1]: “ We map the information to some vector /(qi) in some extended feature (Hilbert) space. Similarly, we map the rating residues, ^riu, to ‘vectors’ in some other Hilbert space. ”; (EN): mapping an input variable to a vector in some Hilbert space is analogous to input variables corresponding to different types) for a particular variable type VARTv a plurality Tv of respective variables VARTv,t is available with t=1, . . . , Tv, ( Ghazanfar [Page 88, Section 4.1, par. 1]: “ To perform a user-based recommendation, we use information qu about users u and try to find a linear mapping Wi to align some extended feature vectors /(qu) to the residue vector wð^riuÞ ”; (EN): information variable q u is analogous to variables VART v,t since q u is sub-variable related to users u, similar to how VART t,v is related to VART v ) for each variable type VARTv only one variable VARTv,t is provided as input variable VARv=VARTv,t, ( Ghazanfar [Page 87, Section 3.2, par. 3]: “ After learning the a parameters, the mapping Wu, can be defined for each user ”) processing the input with the provided modeling function to determine the recommended setting ( Ghazanfar [Page 83, Section 3.1, par. 2]: “ The method developed by Szedmak is to seek a linear mapping between these two spaces which can be used for making predictions. More specifically, in our application, we look for a linear mapping Wu from the space of / vectors to the space of w vectors (refer to Fig. 2). We will use the mapping Wu/(qj) to make a prediction for the rating of a new item j by the user u ”); wherein the modeling function is a function trained based on a Gaussian process with DCOM˜- N (0,K coreg +σ 2 I) defined by a characterizing covariance matrix K coreg and a corresponding characterizing kernel k SEP coreg ( Ghazanfar [Page 87, Section 3.2, par. 2]: “ We can compute the residual kernel, K r , based on the inner products between Gaussian densities functions with expected values ^r and ^r’, and sharing the common standard deviation σ ”; (EN): while the exact matrices are not used in Ghazanfar, the same rationale of training functions based on Gaussian processes is employed.) Regarding Claim 2: Ghazanfar teaches The method according to claim 1, wherein the characterizing kernel kSEP coreg is a separable kernel defined by a product kSEP coreg=Πe=1 nke of sub-kernels ke (Ghazanfar [Page 89, Section 4.3, par. 1]: “ Alternatively we can combine the kernels non-linearly K=Krat * Kdemo * Kfeat; where the * denotes the point-wise product of the kernel matrices ”; (EN): while different variables are used in Ghazanfar, the same concept of a separable kernel defined by a product of sub-kernels is still present). Regarding Claim 3: Ghazanfar teaches The method according to claim 2, wherein each sub-kernel ke measures the similarity between two variables VARTe,t1, VARTe,t2 with t1,t2 ∈ [1, . . . , Te] of the same variable type VARTe ( Ghazanfar [Page 83, Section 3.1, par. 2]: “ in our application, we look for a linear mapping Wu from the space of / vectors to the space of w vectors (refer to Fig. 2). We will use the mapping Wu/(qj) to make a prediction for the rating of a new item j by the user u. To learn the mappings Wu we will minimise the Frobenius norm of Wu ”; [Page 84, Section 3.1, par. 3]: “ Note that minimisation will be achieved when the vectors Wu/(qi) are as uniformly aligned as possible with the vector wð^riuÞ ”). Regarding Claim 5: Ghazanfar teaches The method according to claim 1, further comprising a training to optimize the modeling function ( Ghazanfar [Page 83, Section 3.1, par. 2]: “ in our application, we look for a linear mapping Wu from the space of / vectors to the space of w vectors (refer to Fig. 2). We will use the mapping Wu/(qj) to make a prediction for the rating of a new item j by the user u. To learn the mappings Wu we will minimise the Frobenius norm of Wu ”) . 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. 07-22-aia AIA Claim (s) 8-11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghazanfar as applied to claim 1 above, and further in view of Aparicio et al (WO 2020032947 A1, hereinafter Aparicio) . Regarding Claim 8: Ghazanfar does not distinctly disclose The method according to claim 1, wherein: a first variable type VART1 corresponds to different operating states of an industrial facility; a second variable type VART2 corresponds to different devices of the facility; and in case n≥3 a third variable type VART3 corresponds to different adjustable parameters of the devices. However, Aparicio teaches The method according to claim 1, wherein: a first variable type VART1 corresponds to different operating states of an industrial facility ( Aparicio [0029]: “ a controller sets a manipulated variable of a controlled device of the manufacturing process. More than one variable per device may be set. The variables of multiple devices may be set. The setting is of a value or control signal for controlling operation of the device or devices of the plant. One or more manipulated variables are set. ”); a second variable type VART2 corresponds to different devices of the facility ( Aparicio [0029]: “ a controller sets a manipulated variable of a controlled device of the manufacturing process. More than one variable per device may be set. The variables of multiple devices may be set. The setting is of a value or control signal for controlling operation of the device or devices of the plant. One or more manipulated variables are set. ”); and in case n≥3 a third variable type VART3 corresponds to different adjustable parameters of the devices ( Aparicio [0029]: “ a controller sets a manipulated variable of a controlled device of the manufacturing process. More than one variable per device may be set. The variables of multiple devices may be set. The setting is of a value or control signal for controlling operation of the device or devices of the plant. One or more manipulated variables are set. ”). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems manufacturing process control of Aparicio in order to provide a method for managing a facility via a closed loop control system. The method presented in Aparicio is beneficial for Ghazanfar in that it provides a way for controllers of a facility to adaptively learn the parameters and processes without specific domain knowledge (Aparicio [0003]: “Model-free reinforcement learning is a general-purpose machine learning framework, which enables controllers to adaptively learn the knowledge on controlled processes directly from raw sensor inputs without any hand-engineering features or specific domain knowledge. Then reinforcement learning may reduce the engineering cost and time in programming, tuning, prototyping and commission of control algorithms. While reinforcement learning has recently seen gaining popularity in robotics research, the necessary features for application to manufacturing processes are lacking. The controls output by the reinforcement learned policy may result in unsafe or impractical operation in the plant.”) Regarding Claim 9: Ghazanfar does not distinctly disclose The method according to claim 8, wherein: the settings database contains known and/or assumed settings for observed and/or assumed combinations of adjustable parameters PA, devices DEV, and operating states OS; the recommendation method RM provides, upon receipt of input variables VAR1=OS1, VAR2=DEV1, VAR3=PA1, a recommended setting S for a given adjustable parameter PA1 for a given device DEV1 for a given operating state OS1 of the facility. However, Aparicio teaches The method according to claim 8, wherein: the settings database contains known and/or assumed settings for observed and/or assumed combinations of adjustable parameters PA, devices DEV, and operating states OS ( Aparicio [0029]: “ a controller sets a manipulated variable of a controlled device of the manufacturing process. More than one variable per device may be set. The variables of multiple devices may be set. The setting is of a value or control signal for controlling operation of the device or devices of the plant. One or more manipulated variables are set. ”); the recommendation method RM provides, upon receipt of input variables VAR1=OS1, VAR2=DEV1, VAR3=PA1, a recommended setting S for a given adjustable parameter PA1 for a given device DEV1 for a given operating state OS1 of the facility ( Aparicio [0030]: “ The setting is in response to a change in state, lack of change in state, the determination of state, and/or a change in a value of a controlled variable. For example, the controller determines that the mixer set point is to be at a greater rate. The control signal or set point for the motor of the mixer is changed. This change or an absolute value based on the change is the value that is set ”; (EN): a variable being set based on a change of state and/or a determination of state is analogous to a recommended setting). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems manufacturing process control of Aparicio in order to provide a method for managing a facility via a closed loop control system. The method presented in Aparicio is beneficial for Ghazanfar in that it provides a way for controllers of a facility to adaptively learn the parameters and processes without specific domain knowledge (Aparicio [0003]: “Model-free reinforcement learning is a general-purpose machine learning framework, which enables controllers to adaptively learn the knowledge on controlled processes directly from raw sensor inputs without any hand-engineering features or specific domain knowledge. Then reinforcement learning may reduce the engineering cost and time in programming, tuning, prototyping and commission of control algorithms. While reinforcement learning has recently seen gaining popularity in robotics research, the necessary features for application to manufacturing processes are lacking. The controls output by the reinforcement learned policy may result in unsafe or impractical operation in the plant.”) Regarding Claim 10: Ghazanfar does not distinctly disclose The method according to claim 1, wherein: a first variable type VART1 corresponds to different customers of an industrial product provider; a second variable type VART3 corresponds to different products of the industrial product provider; and in case n≥3 a third variable type VART3 corresponds to different purchase features for purchasing the products. However, Aparicio teaches The method according to claim 1, wherein: a first variable type VART1 corresponds to different customers of an industrial product provider ( Aparicio [0017]: “ In flexible manufacturing (e.g. mass customization), there may not be months to build a process model. Mass customization allows building a unique product for each customer or allows customers to extensively configure the product to their needs from design phase. The constrained reinforcement learning, by building model by itself, may be particularly helpful to flexible manufacturing ”; (EN): while the use of specific variables is not mentioned, allowing customers to “extensively configure the product” implies the use of multiple variables in order to configure the product to the customer’s needs); a second variable type VART3 corresponds to different products of the industrial product provider( Aparicio [0017]: “ In flexible manufacturing (e.g. mass customization), there may not be months to build a process model. Mass customization allows building a unique product for each customer or allows customers to extensively configure the product to their needs from design phase. The constrained reinforcement learning, by building model by itself, may be particularly helpful to flexible manufacturing ”; (EN): while the use of specific variables is not mentioned, allowing customers to “extensively configure the product” implies the use of multiple variables in order to configure the product to the customer’s needs); and in case n≥3 a third variable type VART3 corresponds to different purchase features for purchasing the products ( Aparicio [0017]: “ In flexible manufacturing (e.g. mass customization), there may not be months to build a process model. Mass customization allows building a unique product for each customer or allows customers to extensively configure the product to their needs from design phase. The constrained reinforcement learning, by building model by itself, may be particularly helpful to flexible manufacturing ”; (EN): while the use of specific variables is not mentioned, allowing customers to “extensively configure the product” implies the use of multiple variables in order to configure the product to the customer’s needs). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems manufacturing process control of Aparicio in order to provide a method for managing a facility via a closed loop control system. The method presented in Aparicio is beneficial for Ghazanfar in that it provides a way for controllers of a facility to adaptively learn the parameters and processes without specific domain knowledge (Aparicio [0003]: “Model-free reinforcement learning is a general-purpose machine learning framework, which enables controllers to adaptively learn the knowledge on controlled processes directly from raw sensor inputs without any hand-engineering features or specific domain knowledge. Then reinforcement learning may reduce the engineering cost and time in programming, tuning, prototyping and commission of control algorithms. While reinforcement learning has recently seen gaining popularity in robotics research, the necessary features for application to manufacturing processes are lacking. The controls output by the reinforcement learned policy may result in unsafe or impractical operation in the plant.”) Regarding Claim 11: Ghazanfar does not distinctly disclose Method according to claim 10, wherein: the settings database contains known and/or assumed settings for observed and/or assumed combinations of customers, products, and purchase features; and the recommendation method provides, upon receipt of input variables VAR1=CST1, VAR2=PRD1, VAR3=PCF1, a recommended setting for a given purchase feature PCF1 for a given product PRD1 for a given customer CST1. However, Aparicio teaches Method according to claim 10, wherein: the settings database contains known and/or assumed settings for observed and/or assumed combinations of customers, products, and purchase features ( Aparicio [0018]: “ For training, many (e.g., hundreds or thousands) samples with known ground truth (e.g., settings) are used. The model is trained to output an act or acts for controlling one or more devices in manufacturing. Once trained, data for a particular state of the manufacturing process is applied to the learned network. A computer control system applies the machine-learned network. The learned network outputs the act or acts (e.g., set points or changes in set points) of one or more devices being manipulated to control the manufacture. The computer control system reads input data, calculates an action policy, and injects the outputs into the plant ”); and the recommendation method provides, upon receipt of input variables VAR1=CST1, VAR2=PRD1, VAR3=PCF1, a recommended setting for a given purchase feature PCF1 for a given product PRD1 for a given customer CST1 ( Aparicio [0020]: “ For application after training, the same or different processor receives data representing the state of the manufacturing system and determines an action based on application of the machine-learned policy ”). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems manufacturing process control of Aparicio in order to provide a method for managing a facility via a closed loop control system. The method presented in Aparicio is beneficial for Ghazanfar in that it provides a way for controllers of a facility to adaptively learn the parameters and processes without specific domain knowledge (Aparicio [0003]: “Model-free reinforcement learning is a general-purpose machine learning framework, which enables controllers to adaptively learn the knowledge on controlled processes directly from raw sensor inputs without any hand-engineering features or specific domain knowledge. Then reinforcement learning may reduce the engineering cost and time in programming, tuning, prototyping and commission of control algorithms. While reinforcement learning has recently seen gaining popularity in robotics research, the necessary features for application to manufacturing processes are lacking. The controls output by the reinforcement learned policy may result in unsafe or impractical operation in the plant.”) Regarding Claim 13: Due to claim language similar to that of Claim 1, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below. Ghazanfar does not distinctly disclose a controller to control settings of adjustable parameters of devices of the facility, wherein the respective setting of a particular device depends on an actual operating state of the facility; However, Aparicio teaches a controller to control settings of adjustable parameters of devices of the facility, wherein the respective setting of a particular device depends on an actual operating state of the facility ( Aparicio [0006]: “ A controller is configured to determine a change in a device from a reinforcement machine-learned action policy based on the state. The reinforcement machine-learned action policy used rewards based in part on a limitation of the device ”); Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems manufacturing process control of Aparicio in order to provide a method for managing a facility via a closed loop control system. The method presented in Aparicio is beneficial for Ghazanfar in that it provides a way for controllers of a facility to adaptively learn the parameters and processes without specific domain knowledge (Aparicio [0003]: “Model-free reinforcement learning is a general-purpose machine learning framework, which enables controllers to adaptively learn the knowledge on controlled processes directly from raw sensor inputs without any hand-engineering features or specific domain knowledge. Then reinforcement learning may reduce the engineering cost and time in programming, tuning, prototyping and commission of control algorithms. While reinforcement learning has recently seen gaining popularity in robotics research, the necessary features for application to manufacturing processes are lacking. The controls output by the reinforcement learned policy may result in unsafe or impractical operation in the plant.”) Claim Rejections - 35 USC § 103 07-22-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghazanfar as applied to claim 4 above, and further in view of Zhang et al (US 20210027204 A1, hereinafter Zhang) . Regarding Claim 4: Ghazanfar does not distinctly disclose Method according to claim 2, wherein each sub-kernel is based on a Radial Basis Function (RBF). However, Zhang teaches Method according to claim 2, wherein each sub-kernel is based on a Radial Basis Function (RBF) ( Zhang [0003]: “ The representation of data in this feature space is able to capture nonlinearity in data, e.g., infinite-order interactions among features can be represented in cases of the Gaussian Radial basis function (RBF) kernel ”). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the systems and methods for kernel based recommenders of Ghazanfar with the systems for an interpretable and efficient method and system of kernel learning of Zhang in order to provide a method for kernel based machine learning. The method presented in Zhang is beneficial for Ghazanfar in that it provides a method for improved model prediction accuracy (Zhang [0003]: “The representation of data is one of the essential factors that affect prediction accuracy. Usually, each data example is preprocessed and represented by a feature vector in a feature space. Kernel-based methods are a family of powerful machine learning approaches in terms of prediction accuracy, owing to the capability of mapping each data example to a high-dimensional (possibly infinite) feature space. The representation of data in this feature space is able to capture nonlinearity in data, e.g., infinite-order interactions among features can be represented in cases of the Gaussian Radial basis function (RBF) kernel. Moreover, the feature map in kernel-based methods is implicitly built, and the corresponding inner product can be directly computed via a kernel function. This is known as the “kernel trick”.”) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST. 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, Omar Fernandez Rivas can be reached at (571) 272-2589. 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. /COREY M SACKALOSKY/Examiner, Art Unit 2128 /OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128 Application/Control Number: 18/562,545 Page 2 Art Unit: 2128 Application/Control Number: 18/562,545 Page 3 Art Unit: 2128 Application/Control Number: 18/562,545 Page 4 Art Unit: 2128 Application/Control Number: 18/562,545 Page 5 Art Unit: 2128 Application/Control Number: 18/562,545 Page 6 Art Unit: 2128 Application/Control Number: 18/562,545 Page 7 Art Unit: 2128 Application/Control Number: 18/562,545 Page 8 Art Unit: 2128 Application/Control Number: 18/562,545 Page 9 Art Unit: 2128 Application/Control Number: 18/562,545 Page 10 Art Unit: 2128 Application/Control Number: 18/562,545 Page 11 Art Unit: 2128 Application/Control Number: 18/562,545 Page 12 Art Unit: 2128 Application/Control Number: 18/562,545 Page 13 Art Unit: 2128 Application/Control Number: 18/562,545 Page 14 Art Unit: 2128 Application/Control Number: 18/562,545 Page 15 Art Unit: 2128 Application/Control Number: 18/562,545 Page 16 Art Unit: 2128 Application/Control Number: 18/562,545 Page 17 Art Unit: 2128 Application/Control Number: 18/562,545 Page 18 Art Unit: 2128 Application/Control Number: 18/562,545 Page 19 Art Unit: 2128 Application/Control Number: 18/562,545 Page 20 Art Unit: 2128 Application/Control Number: 18/562,545 Page 21 Art Unit: 2128 Application/Control Number: 18/562,545 Page 22 Art Unit: 2128 Application/Control Number: 18/562,545 Page 23 Art Unit: 2128 Application/Control Number: 18/562,545 Page 24 Art Unit: 2128 Application/Control Number: 18/562,545 Page 25 Art Unit: 2128 Application/Control Number: 18/562,545 Page 26 Art Unit: 2128 Application/Control Number: 18/562,545 Page 27 Art Unit: 2128 Application/Control Number: 18/562,545 Page 28 Art Unit: 2128 Application/Control Number: 18/562,545 Page 29 Art Unit: 2128
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