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. Claim Objections Claims 4-5 are objected to because of the following informalities: Regarding c laim 4 , the claim is objected because the limitation wherein the leakage characteristic matrix is calculated the leakage characteristics matrix using principal component analysis repeats the term “ the leakage characteristic matrix ” the limitation should read: wherein the leakage characteristic matrix is calculated using principal component analysis . Appropriate correction is required. Regarding claim 5 , the claim is objected because the limitation wherein the leakage characteristic matrix is calculated the leakage characteristics matrix using an autoencoder repeats the term “ the leakage characteristic matrix ” the limitation should read: wherein the leakage characteristic matrix is calculated using an autoencoder . Appropriate correction is required. Claim Rejections - 35 USC § 112: Indefiniteness The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claims 7-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 7 , the claim recites the limitation performing clustering on a leakage characteristics matrix and physical to partition the WDN into leakage zones . The claim is unclear and indefinite as the claim because it is unclear whether the term “physical” is relating to the physical characteristics of the WDN or an additional physical constraint . For the purposes of examination, the term “physical” is interpreted as characteristics. Claim 7 also recites the limitation providing a leakage detection machine-learning model, in which the leakage detection machine-learning model is trained based on non-leaking data and configured to detect leakage that occurs in the WDN based on; . The claim is unclear and indefinite as the claim appears to include an additional “based on” after stating the model is used to detect leakages. For the purposes of examination, the “based on” at the end of the limitation is interpreted as a typo. Regarding claims 8-9 , the claims are rejected for at least their dependence on claim 7. 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- 9 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim 1 , in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A system for leak detection and localization, comprising: a water distribution network (WDN) partition stage programmed … and a leakage monitoring stage programmed . A system claim that has two software stages is interpreted as computer program. Therefore, the claimed invention in claim 1 is directed to nonstatutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a computer program is interpreted as “software per se” which is not one of the four statutory categories (MPEP 2106.03). Applicant is encouraged to amend the claim into one of the four statutory categories. For the purposes of compact prosecution, the additional steps of the 101 analysis will be performed below. In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components: a water distribution network (WDN) partition stage programmed to cluster a WDN model into partition zones … (i.e., the broadest reasonable interpretation includes a step of observation, evaluation , and judgement and could be performed mentally or with pen and paper like grouping zones based on water pressure , which is either a mental process of observation/ evaluation/judgement (MPEP 2106) ). and a leakage monitoring stage programmed to ( i ) detect the occurrence of leakage in the WDN and provide leak detection data … (i.e., the broadest reasonable interpretation includes a step of observation, evaluation , and judgement and could be performed mentally or with pen and paper like identifying leaks based on high water pressure values , which is either a mental process of observation/ evaluation/judgement (MPEP 2106) ). If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea. In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application : … based on applying a modified k-means clustering algorithm to leakage characteristic data and physical connectivity data; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) ). … based on applying a trained unsupervised leakage detection machine-learning model to the partition zones and sensor data, (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) ). and/or (ii) provide localization data to identify a location of a leakage zone in the WDN based on providing the leak detection data to a localization machine-learning model. (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g)) ). Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea. In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception: Regarding limitation (V), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)). Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II). Further, limitation ( III ), under the broadest reasonable interpretation, merely recite steps that apply a generic clustering algorithm to perform a judicial exception , which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly , limitation ( IV ), under the broadest reasonable interpretation, merely recite steps that apply a generic unsupervised model to perform a judicial exception , which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. Regarding claim 2 , it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein the sensor data includes water pressure data. Under the broadest reasonable interpretation, merely recite steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)) . Therefore, claim 2 does not solve the deficiencies of claim 1 . Regarding claim 3 , it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein the leakage characteristic data includes a leakage characteristic matrix. Under the broadest reasonable interpretation, the limitations recite using a matrix which is interpreted as using a mathematical relationship . A mathematical relationship is interpreted as a mathematical concept. Therefore, claim 3 does not solve the deficiencies of claim 1. Regarding claim 4 , it is dependent upon claim 3 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the leakage characteristic matrix is calculated the leakage characteristics matrix using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix based on a training dataset of non-leaking data and a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. Under the broadest reasonable interpretation, the limitations recite using a PCA calculation to calculate a matrix which is interpreted as using a mathematical calculation . A mathematical calculation is interpreted as a mathematical concept. Therefore, claim 4 does not solve the deficiencies of claim 3 . Regarding claim 5 , it is dependent upon claim 3 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein the leakage characteristic matrix is calculated the leakage characteristics matrix using an autoencoder (AE) neural network to provide an AE-based leakage characteristics matrix based on a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. U nder the broadest reasonable interpretation, the limitations merely recite steps that apply a generic autoencoder to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 5 does not solve the deficiencies of claim 3. Regarding claim 6 , it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites wherein the leakage detection machine-learning model further comprises at least one of a principal component analysis (PCA) machine-learning model or an autoencoder machine learning model, in which the PCA and/or autoencoder machine learning models are trained based on non-leaking data. U nder the broadest reasonable interpretation, the limitations merely recite steps that apply a generic autoencoder or PCA model to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 6 does not solve the deficiencies of claim 1 . Regarding claim 7 , in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A computer-implemented method . The claim recites a method. A method is one of the four statutory categories of invention. In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components: performing clustering on a leakage characteristics matrix and physical to partition the WDN into leakage zones, in which the leakage characteristics matrix describes the leakage behaviors of each of a plurality of junctions in the WDN; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation , and judgement and could be performed mentally or with pen and paper like grouping zones based on water pressure , which is either a mental process of observation/ evaluation/judgement (MPEP 2106) ). determining centroids of partitioned clusters and leakage zones; (i.e., the broadest reasonable interpretation includes a mathematical calculation of calculating centroids , a mathematical calculation is considered a mathematical concept (MPEP 2106) ). … detect leakage that occurs in the WDN based on; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation , and judgement and could be performed mentally or with pen and paper like identifying leaks based on high water pressure values , which is either a mental process of observation/ evaluation/judgement (MPEP 2106) ). … to locate a leakage zone in the WDN. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation , and judgement and could be performed mentally or with pen and paper like identifying the location associated with a leak , which is either a mental process of observation/ evaluation/judgement (MPEP 2106) ). If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea. In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application : accessing, … , a water distribution network (WDN) model, the WDN model representing structural, physical, topological, hydraulic characteristics of the WDN; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g)) ). … from non-transitory computer-readable memory … (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) ). providing a leakage detection machine-learning model, in which the leakage detection machine-learning model is trained based on non-leaking data and configured to … (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) ). and providing a leakage localization machine-learning model, in which the leakage localization machine-learning model is trained based on labeled leakage data and configured … (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) ). Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea. In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception: Regarding limitation (V), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)). Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II). Further, limitation ( VI ), under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform a judicial exception , which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Further, limitation ( VII ), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model to perform a judicial exception , which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly , limitation ( VIII ), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model to perform a judicial exception , which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. Regarding claim 8 , it is dependent upon claim 7 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 8 recites further comprising calculating the leakage characteristics matrix using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix. Under the broadest reasonable interpretation, the limitations recite using a PCA calculation to calculate a matrix which is interpreted as using a mathematical calculation . A mathematical calculation is interpreted as a mathematical concept. Therefore, claim 8 does not solve the deficiencies of claim 7 . Regarding claim 9 , it is dependent upon claim 7 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 9 recites further comprising calculating the leakage characteristics matrix using an autoencoder neural network. U nder the broadest reasonable interpretation, the limitations merely recite steps that apply a generic autoencoder to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 9 does not solve the deficiencies of claim 7. Claims 1 0-11 are rejected under 35 U.S.C 101 because the claimed invention is directed to non - stat ut ory subject matter . Regarding claim 1 0 , in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A system comprising: a graph convolution neural network (GCN) model … and a deep reinforcement learning method programmed to . A system with two machine learning models is interpreted as a computer program. Therefore, the claimed invention in claim 1 0 is directed to non - statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a computer program is interpreted as “software per se” which is not one of the four statutory categories (MPEP 2106.03). Applicant is encouraged to amend the claim into one of the four statutory categories. Regarding claim 11 , it is dependent upon claim 10 and fails to resolve the deficiencies identified above by placing the invention into a statutory category . Claim Rejections - 35 USC § 102 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 – (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. (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. Claim s 1 -3 and 6 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Chen, et al., Non-Patent Literature “ An iterative method for leakage zone identification in water distribution networks based on machine learning ” (“Chen”) . Regarding claim 1 , The examiner notes that the claim is written in an alternative embodiment format and selects the (II) embodiment . Chen discloses: A system for leak detection and localization, comprising: a water distribution network (WDN) partition stage programmed to cluster a WDN model into partition zones based on applying a modified k-means clustering algorithm to leakage characteristic data and physical connectivity data; (Chen, pg. 1940 col. 1, “As shown in Figure 1, the proposed method can be performed in the following steps: (1) EPANET software is used to establish a hydraulic model [ and physical connectivity data; ] of the WDN;23 [ A system for leak detection and localization, comprising: a water distribution network (WDN) partition stage ] (2) assuming that l (l>=1) simultaneous leakages occur and the leakage characteristic (leakage sample, the residual vector between normal and abnormal sensor values)…(3) the leakage matrix of the identified leakage zone is generated; [ to leakage characteristic data ] (4) the k-means clustering is used to divide the identified leakage zone into two parts according to the leakage matrix; [ programmed to cluster a WDN model into partition zones based on applying a modified k-means clustering algorithm ]”). and a leakage monitoring stage programmed to ( i ) detect the occurrence of leakage in the WDN and provide leak detection data based on applying a trained unsupervised leakage detection machine-learning model to the partition zones and sensor data, and/or (ii) provide localization data to identify a location of a leakage zone in the WDN based on providing the leak detection data to a localization machine-learning model. (Chen, pg. 1940 col. 1 and Figure 1, “(7) each leakage combination of the candidate leakage zones is used as a category label of the classifier model, and the selected features are used to train the RF classifier; and (8) if the final accuracy (Acc) of the model is greater than 95%, DSl is input into the trained RF classifier, and the leakage zones and the number of leakage nodes in each leakage zone are output [ and a leakage monitoring stage programmed to… and/or (ii) provide localization data to identify a location of a leakage zone in the WDN based on providing the leak detection data to a localization machine-learning model. ]”). Regarding claim 2 , Chen discloses the system of claim 1 . Chen further discloses wherein the sensor data includes water pressure data. (Chen, pg. 1942 col. 2, “ Because there are flow sensors and pressure sensors in WDNs [ wherein the sensor data includes water pressure data. ]”). Regarding claim 3 , Chen discloses the system of claim 1 . Chen further discloses wherein the leakage characteristic data includes a leakage characteristic matrix. (Chen, pg. 1940 col. 1, “ (3) the leakage matrix of the identified leakage zone is generated [ wherein the leakage characteristic data includes a leakage characteristic matrix. ]”). Regarding claim 6 , the claim is directed to a non-selected alternative embodiment and therefore is not considered. The claim is rejected for at least its dependence on claim 1. Claim Rejections - 35 USC § 103 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. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, et al., Non-Patent Literature “ An iterative method for leakage zone identification in water distribution networks based on machine learning ” (“Chen”) in view of Quinones- Grueiro , et al., Non-Patent Literature “An Unsupervised Approach t o Leak Detection and Location i n Water Distribution Networks” (“ Quinones- Grueiro ”) . Regarding claim 4 , Chen teaches the system of claim 3 . Chen further teaches wherein the leakage characteristic matrix is calculated the leakage characteristics matrix … and a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. (Chen, pg. 1940 col. 2, “ Leakage matrix [ wherein the leakage characteristic matrix is calculated the leakage characteristics matrix ] For a network, assume that there are N nodes, NP pressure sensors, and NQ flow sensors. The normal values of the sensors are calculated as s = [ P0 1, P0 2, :::, P0 NP , Q0 1, Q0 2, :::, Q0 NQ ] . When l simultaneous leakages occur, the abnormal values of the sensors are Sl = [ Pl1,Pl2,:::, PlNP , Ql1,Ql2,:::, QlNQ ] . Then, the variations in the sensor values are DSl = [ Pl1 P0 1,Pl2 P0 2,:::, PlNP P0 Np, Ql1 Q0 1,Ql2 Q0 2, :::, QlNQ Q0 NQ ] . Leakage flow can be represented as a power function of the nodal pressure [ and a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. ] as24 Q = CPr ”). While Chen teaches a system for determining leak locations using a leak characteristic matrix, Chen does not explicitly teac h … using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix based on a training dataset of non-leaking data … . Quinones- Grueiro teaches … using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix based on a training dataset of non-leaking data … ( Quinones- Grueiro , pg. 286 col. 1, “ The historical data set of hydraulic signals is then organized as a matrix formed by p observations of the vector x(t), which can be represented as ⎡ ⎢ X = ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ x(1) x(2) . . . x(p) ⎥ ⎦ = ⎡ ⎢ ⎢ ⎢ x1(1) x2(1) ... xm (1) x1(2) x2(2) ... xm (2) . ⎣ . . x1(p) x2(p) ... xm (p) ⎤ ⎥ ⎥ . . . . . . . . . ⎥ ⎦ ∈ Rp× m. (9) The goal of the classical PCA method is to find a linear transformation matrix P ∈ Rm×a that projects each vector of variables x(t) from X to a space where the process information in terms of variability is preserved [ using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix ]”, and Quinones- Grueiro , pg. 285 col. 1, “ The PCA-based leak location strategy allows estimating the contribution of each variable for identifying the potential zone where the leak occurs. The main advantages of this approach are the following: ( i ) it only requires hydraulic data from the DMA operating under normal conditions [ based on a training dataset of non-leaking data ]”). Chen and Quinones- Grueiro are both in the same field of endeavor (i.e. water leakage ). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chen and Quinones- Grueiro to teach the above limitation(s). The motivation for doing so is that using PCA reduces the need to provide labeled data to determine leaks (cf. Quinones- Grueiro , pg. 284 col. 2, “ Ultimately, obtaining a data set of all the possible leak scenarios is not a feasible task even for networks with a model that is available. The drawbacks mentioned above motivate this paper, and the main contribution is a fault detection and location approach that considers an unsupervised method. ”). Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, et al., Non-Patent Literature “ An iterative method for leakage zone identification in water distribution networks based on machine learning ” (“Chen”) in view of Wang, et al., Non-Patent Literature “ Auto-encoder based dimensionality reduction ” (“Wang”) . Regarding claim 5 , Chen teaches the system of claim 3 . Chen further teaches wherein the leakage characteristic matrix is calculated the leakage characteristics matrix … and a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. (Chen, pg. 1940 col. 2, “ Leakage matrix [ wherein the leakage characteristic matrix is calculated the leakage characteristics matrix ] For a network, assume that there are N nodes, NP pressure sensors, and NQ flow sensors. The normal values of the sensors are calculated as s = [ P0 1, P0 2, :::, P0 NP , Q0 1, Q0 2, :::, Q0 NQ ] . When l simultaneous leakages occur, the abnormal values of the sensors are Sl = [ Pl1,Pl2,:::, PlNP , Ql1,Ql2,:::, QlNQ ] . Then, the variations in the sensor values are DSl = [ Pl1 P0 1,Pl2 P0 2,:::, PlNP P0 Np, Ql1 Q0 1,Ql2 Q0 2, :::, QlNQ Q0 NQ ] . Leakage flow can be represented as a power function of the nodal pressure [ and a leakage matrix of monitored pressure when leakage occurs at respective junctions in the WDN. ] as24 Q = CPr ”). While Chen teaches a system for determining leak locations using a leak characteristic matrix, Chen does not explicitly teach …using an autoencoder (AE) neural network to provide an AE-based leakage characteristics matrix … . Wang teaches …using an autoencoder (AE) neural network to provide an AE-based leakage characteristics matrix … ( Wang, pg. 233 col. 1, “ In this section, we briefly introduce auto-encoder, four representative dimensionality reduction methods and the concept of dimensionality reduction and intrinsic dimensionality … Suppose the original input x belongs to n-dimensional space and the new representation y belongs to m-dimensional space, an auto-encoder is a special and tricky three-layered neural network in which we set the output h wb (x) = (x ~ 1, x ~ 2,…, x ~ n )T equal to the input x = (x1, x2,…, xn )T [ …using an autoencoder (AE) neural network to provide an AE-based leakage characteristics matrix … ] . ” ) . Chen and Wang are both in the same field of endeavor (i.e. data processing ). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chen and Wang to teach the above limitation(s). The motivation for doing so is that u sing an autoencoder can reduce the dimensionality of the data and find relationships between data points (cf. Wang, pg. 242 col. 1, “ In some cases, auto-encoder not only reduces dimensionality, but can also detect repetitive structures. ”). Claim s 7 -8 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, et al., Non-Patent Literature “ An iterative method for leakage zone identification in water distribution networks based on machine learning ” (“Chen”) in view of Quinones- Grueiro , et al., Non-Patent Literature “An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks” (“ Quinones- Grueiro ”) and further in view of Cussonneau , et al., US Pre-Grant Publication US20180181111A1 (“ Cussonneau ”) . Regarding claim 7 , Chen discloses: A computer-implemented method, (Chen, pg. 1948 col. 2, “All the computations summarized in this article were performed using an Intel(R) Core(TM) i7-6700CPU@ 3.40GHz, with 32 GB of RAM memory. A Windows 10 Home 64-bit operating system [ A computer-implemented method, ]”) . comprising: accessing, … , a water distribution network (WDN) model, the WDN model representing structural, physical, topological, hydraulic characteristics of the WDN; (Chen, pg. 1940 col. 1, “As shown in Figure 1, the proposed method can be performed in the following steps: (1) EPANET software is used to establish a hydraulic model of the WDN [ comprising: accessing, … , a water distribution network (WDN) model, the WDN model representing structural, physical, topological, hydraulic characteristics of the WDN; ]”). performing clustering on a leakage characteristics matrix and physical to partition the WDN into leakage zones, in which the leakage characteristics matrix describes the leakage behaviors of each of a plurality of junctions in the WDN; (Chen, pg. 1940 col. 1, “(2) assuming that l (l>=1) simultaneous leakages occur and the leakage characteristic (leakage sample, the residual vector between normal and abnormal sensor values)…(3) the leakage matrix of the identified leakage zone is generated; [ in which the leakage characteristics matrix describes the leakage behaviors of each of a plurality of junctions in the WDN; ] (4) the k-means clustering is used to divide the identified leakage zone into two parts according to the leakage matrix; [ performing clustering on a leakage characteristics matrix and physical to partition the WDN into leakage zones, ]”). determining centroids of partitioned clusters and leakage zones; (Chen, pg. 1940 col. 1, “(4) the k-means clustering is used to divide the identified leakage zone into two parts according to the leakage matrix; [ determining centroids of partitioned clusters and leakage zones; ]”). … and providing a leakage localization machine-learning model, in which the leakage localization machine-learning model is trained based on labeled leakage data and configured to locate a leakage zone in the WDN. (Chen, pg. 1940 col. 1 and Figure 1, “(7) each leakage combination of the candidate leakage zones is used as a category label [ in which the leakage localization machine-learning model is trained based on labeled leakage data ] of the classifier model, and the selected features are used to train the RF classifier; [ … and providing a leakage localization machine-learning model, ] and (8) if the final accuracy (Acc) of the model is greater than 95%, DSl is input into the trained RF classifier, and the leakage zones and the number of leakage nodes in each leakage zone are output [ and configured to locate a leakage zone in the WDN. ]”). While Chen teaches a system running on a computer for determining leak locations using a leak characteristic matrix and a classifier model, Chen does not explicitly teach using an unsupervised learning model or a non-transitory storage medium: … from non-transitory computer-readable memory … providing a leakage detection machine-learning model, in which the leakage detection machine-learning model is trained based on non-leaking data and configured to detect leakage that occurs in the WDN based on; Quinones- Grueiro teaches providing a leakage detection machine-learning model, in which the leakage detection machine-learning model is trained based on non-leaking data and configured to detect leakage that occurs in the WDN based on; ( Quinones- Grueiro , pg. 285 col. 1, “ The PCA-based leak location strategy allows estimating the contribution of each variable for identifying the potential zone where the leak occurs [ providing a leakage detection machine-learning model, … and configured to detect leakage that occurs in the WDN based on; ] . The main advantages of this approach are the following: ( i ) it only requires hydraulic data from the DMA operating under normal conditions [ in which the leakage detection machine-learning model is trained based on non-leaking data ]”). Chen and Quinones- Grueiro are both in the same field of endeavor (i.e. water leakage ). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chen and Quinones- Grueiro to teach the above limitation(s). The motivation for doing so is that using PCA reduces the need to provide labeled data to determine leaks (cf. Quinones- Grueiro , pg. 284 col. 2, “ Ultimately, obtaining a data set of all the possible leak scenarios is not a feasible task even for networks with a model that is available. The drawbacks mentioned above motivate this paper, and the main contribution is a fault detection and location approach that considers an unsupervised method. ”). While Chen in view of Quinones- Grueiro teaches a system running on a computer for determining leak locations using a leak characteristic matrix and supervised/unsupervised models, the combination does not explicitly teach: … from non-transitory computer-readable memory … Cussonneau teaches …from non-transitory computer-readable memory… ( Cussonneau , ⁋32, “ The invention also discloses a computer program product, stored on a non-transitory computer-readable medium [ …from non-transitory computer-readable memory… ] , detecting anomalies in a water distribution system ”). Chen, in view of Quinones- Grueiro , and Cussonneau are both in the same field of endeavor (i.e. water leaks ). Chen, in view of Quinones- Grueiro , teaches a base method for determining leak locations using machine learning . Cussonneau teaches a known technique of using a non-transitory computer-readable medium to store machine learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chen, in view of Quinones- Grueiro , and Cussonneau to teach the above limitation(s). The motivation for doing so is that applying Cussonneau ’s known technique of using a non-transitory computer-readable medium to store machine learning functions to A’s base system of determining leak locations using machine learning would yield predictable results . Regarding claim 8 , Chen in view of Quinones- Grueiro and Cussonneau teaches the method of claim 7 . Quinones- Grueiro further teaches further comprising calculating the leakage characteristics matrix using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix. ( Quinones- Grueiro , pg. 286 col. 1, “ The historical data set of hydraulic signals is then organized as a matrix formed by p observations of the vector x(t), which can be represented as ⎡ ⎢ X = ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ x(1) x(2) . . . x(p) ⎥ ⎦ = ⎡ ⎢ ⎢ ⎢ x1(1) x2(1) ... xm (1) x1(2) x2(2) ... xm (2) . ⎣ . . x1(p) x2(p) ... xm (p) ⎤ ⎥ ⎥ . . . . . . . . . ⎥ ⎦ ∈ Rp× m. (9) The goal of the classical PCA method is to find a linear transformation matrix P ∈ Rm×a that projects each vector of variables x(t) from X to a space where the process information in terms of variability is preserved [ further comprising calculating the leakage characteristics matrix using principal component analysis (PCA) to provide a PCA-based leakage characteristics matrix. ]”). It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Quinones- Grueiro with the teachings of Chen and Cussonneau for the same reasons disclosed in claim 7 . Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, et al., Non-Patent Literature “An iterative method for leakage zone identification in water distribution networks based on machine learning” (“Chen”) in view of Quinones- Grueiro , et al., Non-Patent Literature “An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks” (“ Quinones- Grueiro ”) and further in view of Cussonneau , et al., US Pre-Grant Publication US20180181111A1 (“ Cussonneau ”) and Wang, et al., Non-Patent Literature “Auto-encoder based dimensionality reduction” (“Wang”). Regarding claim 9 , Chen in view of Quinones- Grueiro and Cussonneau teaches the method of claim 7 . While the combination teaches a system for determining leak locations using a leak characteristic matrix, the combination does not explicitly teach further comprising calculating the leakage characteristics matrix using an autoencoder neural network. Wang teaches further comprising calculating the leakage characteristics matrix using an autoencoder neural network. (Wang, pg. 233 col. 1, “ In this section, we briefly introduce auto-encoder, four representative dimensionality reduction methods and the concept of dimensionality reduction and intrinsic dimensionality … Suppose the original input x belongs to n-dimensional space and the new representation y belongs to m-dimensional space, an auto-encoder is a special and tricky three-layered neural network in which we set the output h wb (x) = (x~1, x~2,…, x~n )T equal to the input x = (x1, x2,…, xn )T [ further comprising calculating the leakage characteristics matrix using an autoencoder neural network. ] . ”). Chen, in view of Quinones- Grueiro and Cussonneau , and Wang are both in the same field of endeavor (i.e. data processing ). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Chen, in view of Quinones- Grueiro and Cussonneau , and Wang to teach the above limitation(s). The motivation for doing so is that u sing an autoencoder can reduce the dimensionality of the data and find relationships between data points (cf. Wang, pg. 242 col. 1, “ In some cases, auto-encoder not only reduces dimensionality, but can also detect repetitive structures. ”). Claim s 10 -11 are rejected under 35 U.S.C. 103 as being unpatentable over Yang , et al., Foreign Patent Publication CN114245337A (“ Yang ”), please see the provided translated version for art mapping purposes , in view of Zhao, et al., Non-Patent Literature “ Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning ” (“Zhao”) . Regarding claim 10 , Yang discloses A system comprising: a graph convolution neural network (GCN) model trained to encode a water distribution network (WDN) based on topology and performance of service nodes of the WDN, the GCN model configured to provide GCN output data representative of repair actions in the WDN; ( Yang , pg. 9 , “The purpose of the present invention is to design and develop a method for arranging leak location sensors in water supply pipe network based on graph convolution network [ A system comprising: a graph convolution neural network (GCN) model ], which combines graph analysis and topological structure information of water supply distribution network to solve the problem of optimal arrangement of sensors for leak detection [ trained to encode a water distribution network (WDN) based on topology and performance of service nodes of the WDN, ]. The influence of the occurrence of leakage events at each node in the actual water supply distribution network improves the accuracy of sensor placement [ the GCN model configured to provide GCN output data representative of repair actions in the WDN; ].”). While Yang teaches a system that uses a GCN to encode a water distribution network for optimal leak sensor placement, Yang does not explicitly teach and a deep reinforcement learning method programmed to train parameters of the GCN model based on a measure of resilience determined from the outputs of GCN model representative of reward values corresponding to respective repair actions. Zhou teaches and a deep reinforcement learning method programmed to train parameters of the GCN model based on a measure of resilience determined from the outputs of GCN model representative of reward values corresponding to respective repair actions. (Zhao, pg. 1602 col. 1, “ In this work, we establish a general DRL framework equipped with graph convolutional networks (GCN) called Graph-Reinforcement Learning (G-RL) to address the challenges mentioned above. GCN takes the feature matrix of nodes and edges represented by a graph as the input and produces a node-level feature matrix [22]. It draws increasing attention to solve problems over graphs [23] … These graphical features are taken as the input to an RL module to guide the DG decision-making [ and a deep reinforcement learning method programmed to train parameters of the GCN model ] . ”, and Zhao, pg. 1603 col. 2, “ 4) Reward Ri,t ( st,at ): The proposed formulation covers various DSR problems, such as the sequential restoration model [8], [13] and critical load restoration [29], [30]. Given a specific restoration problem, the reward function Ri, t( st,at ) is defined as its corresponding objective such as maximizing the overall load supplied or minimizing the outage time of critical load supply [ based on a measure of resilience determined from the outputs of GCN model representative of reward values corresponding to respective repair actions. ]”). A person having ordinary skill in the art would reasonably find the teachings of Zhao to solve the problem of using reinforcement learning to train a GCN for repair location identification present in Yang . In view of the teachings of Zhao it would have been obvious for a person of ordinary skill in the art to apply the teachings of Zhao to Yang before the effective filing date of the claimed invention in order to improve leak sensor placement by leveraging the decision making of reinforcement learning (cf. Zhao, pg. 1602 col. 1, “ agents in DRL need to make decisions based on a complete knowledge of the system structure and conditions, by which they can understand the underlying factors that affect t heir decision-making. ”). Regarding claim 11 , Yang in view of Zhao teaches the system of claim 10 . Zhao further teaches wherein the deep reinforcement learning method is further programmed to select an optimal repair sequence for the WDN. (Zhao, abstract, “ A distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance [ wherein the deep reinforcement learning method is further programmed to select an optimal repair sequence for the WDN. ] . ”). It would have been obvious to one of ordinary skill in the art before the effective filling date of th