DETAILED ACTION The following is an initial Office Action upon examination of the above-identified application on the merits. Claims 1-12 are pending in this application. 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 Interpretation Claim 12 recites “… storing instructions, when executed by one or more processors, configured to perform the method of claim 11” (lines 1-2). The recitation of “when” does not positively recite the subsequent limitation of “execute” as occurring. Suggested claim language: “… storing instructions, executed by one or more processors, configured to perform the method of claim 11 ” . Claim Objections Claim 12 is objected to because of the following informalities: Claim 12 includes an extra space between “perform” and “the method”. Appropriate correction is required. 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 1-12 are FILLIN "Pluralize the word 'Claim' if necessary and then identify the claim(s) being rejected." rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1: At step 1, the recites “(a)n artificial intelligence based filed installation control system”, therefore is a machine, which is a statutory category of invention . At step 2A, prong one, the claim recites “… analyze the data collected by the data collection subsystem ” and “… analyze the data processed by the hybrid digital twin model …”. The limitation s of “… analyze the data collected by the data collection subsystem ” and “… analyze the data processed by the hybrid digital twin model …” , as drafted, is a process, under its broadest reasonable interpretation covers performing the limitation in the mind. Where, nothing in the claim precludes the step s from being practically performed in the mind. For example, “ analyze ” in the context of the claim encompasses evaluating data to additional identify information (MPEP 2106.04(a)(2): The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.) If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. At step 2A, prong two, the claim recites “ a data collection subsystem configured to collect installation operation data, from one or more field installations”; “a data analysis subsystem … ”; “a control subsystem configured to control the one or more field installations, based on an output of the data analysis subsystem …”; “… the data collection subsystem, the data analysis subsystem, and the control subsystem are communicatively connected to each other through a network …”; and “… the data analysis subsystem comprises: a data processing module configured to process the data collected by the data collection subsystem; a hybrid digital twin model configured to process the data processed by the data processing module; and a signal generation module configured to analyze the data processed by the hybrid digital twin model and output a control information signal, and wherein the hybrid digital twin model is a fusion of an artificial intelligence learning and inference model, which is based on installation operation data, with a physical model regarding the one or more field installations, and is trained based on installation operation data regarding the one or more field installations ” . The limitations of “ … a data collection subsystem …”; “ a data analysis subsystem … ”; “a control subsystem …” ; “… the data collection subsystem, the data analysis subsystem, and the control subsystem are communicatively connected to each other through a network …” ; and “… the data analysis subsystem comprises: a data processing module configured to process the data collected by the data collection subsystem; a hybrid digital twin model configured to process the data processed by the data processing module; and a signal generation module …, and wherein the hybrid digital twin model is a fusion of an artificial intelligence learning and inference model, which is based on installation operation data, with a physical model regarding the one or more field installation s … ” are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)). The limitation of “ on e or more field installations ” is recited at a high level of generality and merely limits the abstract idea to a field of use (i.e. a field installation control system ). The Courts have found “a claim directed to a judicial exception cannot be made eligible ‘simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.’ Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05(h)). The limitation s of “… collect installation operation data, from one or more field installations ” and “the hybrid digital twin model … is trained based on installation operation data regarding the one or more field installations” represent mere data gathering. The limitation s of “ collect ” and “trained” are recited at a high level of generality and recited so generically they represent no more than an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)). The limitation of “ … control the one or more field installations, based on an output of the data analysis subsystem …” is a recitation of the words “apply it” (or an equivalent). “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965).” (see MPEP 2106.05(f)) The limitation of “ … output a control information signal … ” represent s the mere output of data. The limitation is recited at a high level of generally and recited so generically it represent s no more than an insignificant extra-solution activity of outputting data (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea. At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the addition of the elements of “ … a data collection subsystem …”; “ a data analysis subsystem … ”; “a control subsystem …” ; “… the data collection subsystem, the data analysis subsystem, and the control subsystem are communicatively connected to each other through a network …” and “… the data analysis subsystem comprises: a data processing module configured to process the data collected by the data collection subsystem; a hybrid digital twin model configured to process the data processed by the data processing module; and a signal generation module …, and wherein the hybrid digital twin model is a fusion of an artificial intelligence learning and inference model, which is based on installation operation data, with a physical model regarding the one or more field installations …” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer ‐ implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).” The additional element of “ one or more field installations ” merely limit s the abstract idea to a field of use. Wherein, limiting the invention to a field of use cannot provide an inventive concept. Thus, the claim is not patent eligible. (MPEP 2106.05(h)). The limitation s of “… collect installation operation data, from one or more field installations” and “the hybrid digital twin model … is trained based on installation operation data regarding the one or more field installations” , as discussed above, amount to no more than mere data gathering and are insignificant extra-solution activit ies . Further, the limitation s are well-understood, routine and conventional; wherein the courts have found limitations directed to obtaining data recited at a high level of generality to be well-understood, routine and conventional . See MPEP 2106.05(d)(II), “storing and retrieving information in memory”. The limitation of “ … control the one or more field installations, based on an output of the data analysis subsystem …” represent s an equivalent recitation of the phrase “apply it”, wherein the courts have identified limitations that “(m)erely recit(e) the words ‘apply it’ (or an equivalent)” with the judicial exception cannot provide an inventive concept …”. (see MPEP 2106.04(d)(I)). The limitation of “ … output a control information signal … ” , as discussed above, represent s an insignificant extra-solution activity of outputting data. Further, the courts have found limitations directed to outputting data recited at a high level of generality to be well-understood, routine and conventional . See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible. Claim 2 : The limitation of claim 2 further details “ the installation operation data ” of claim 1; hence, the claim stands rejected under the same rationale as set forth in claim 1. Claim 3: At step 2A, prong two, the claim recites “… a model training subsystem configured to train the hybrid digital twin model, based on the installation operation data regarding the one or more field installations ”. The limitation of “… a model training subsystem …” is recited at a high level of generality and recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)). The limitation of “… train the hybrid digital twin model, based on the installation operation data regarding the one or more field installations ” represents mere data gathering. The limitation of “train” is recited at a high level of generality and recited so generically it represent s no more than an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea. At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the addition of the element of “… a model training subsystem …” amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer ‐ implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).” The limitation of “… train the hybrid digital twin model, based on the installation operation data regarding the one or more field installations ”, as discussed above, amounts to no more than mere data gathering and is an insignificant extra-solution activity. Further, the limitation is well-understood, routine and conventional; wherein the courts have found limitations directed to obtaining data recited at a high level of generality to be well-understood, routine and conventional . See MPEP 2106.05(d)(II), “storing and retrieving information in memory”. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible. Claim 4: The limitation of claim 4 further details “ the artificial intelligence learning and inference model” of claim 1; hence, the claim stands rejected under the same rationale as set forth in claim 1. Claim 5 : The limitation s of claim 5 further details “ the data collection subsystem”; “ the data analysis subsystem”; and “the control subsystem” of claim 1; hence, the claim stands rejected under the same rationale as set forth in claim 1. Claim 6 : The limitation of claim 6 further details “ the physical model ” of claim 1; hence, the claim stands rejected under the same rationale as set forth in claim 1. Claim 7 : The limitation of claim 7 further details “the control subsystem” of claim s 1 and 5 ; hence, the claim stands rejected under the same rationale as set forth in claim s 1 and 5 . Claim 8 : The limitation of claim 8 further details “the hybrid digital twin model” of claim 1; hence, the claim stands rejected under the same rationale as set forth in claim 1. Claim 9: At step 2A, prong two, the claim recites “… a data generation subsystem configured to generate virtual installation operation data regarding the one or more field installations …” and “… the hybrid digital twin model further comprises a model trained based on the virtual installation operation data regarding the one or more field installations ” . The limitation s of “… a data generation subsystem …” and “… the hybrid digital twin model further comprises a model trained based on the virtual installation operation data regarding the one or more field installations ” are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)). The limitation of “… generate virtual installation operation data regarding the one or more field installations … ” represents mere data gathering. The limitation of “train” is recited at a high level of generality and recited so generically it represent s no more than an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea. At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the addition of the element s of “… a data generation subsystem …” and “… the hybrid digital twin model further comprises a model trained based on the virtual installation operation data regarding the one or more field installations ” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer ‐ implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).” The limitation of “… generate virtual installation operation data regarding the one or more field installations … ” , as discussed above, amounts to no more than mere data gathering and is an insignificant extra-solution activity. Further, the limitation is well-understood, routine and conventional ; wherein the courts have found limitations directed to obtaining data recited at a high level of generality to be well-understood, routine and conventional . See MPEP 2106.05(d)(II), “storing and retrieving information in memory”. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible. Claim 10 : The limitation of claim 10 further details “ the data generating subsystem ” of claim 9 ; hence, the claim stands rejected under the same rationale as set forth in claim 9 . Claim 11 : Claim 11 represents an equivalent method claim to claim 1 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 1. Claim 12 : Claim 12 represents an equivalent non-transitory computer-readable recording medium claim to claim 1 1 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 1 1 . 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. 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. Claims 1 -3 , 5, 7 , 8 , 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2020/0118053 (hereinafter Chapin ) in view of U.S. Patent Publication No. 2019/0064787 A1 (hereinafter Maturana) . As per claim 1, Chapin substantially teaches the Applicant’s claimed invention. Chapin teaches the limitations of an artificial intelligence based field installation control system for process operation optimization, the field installation control system comprising: a data collection subsystem (Fig. 4, element 404; i.e. database) configured to collect installation operation data (Fig. 4; element 114; i.e. asset data) , from one or more field installations (pgs. 2-3, par. [0025] and pgs. 6-7, par. [0041]; i.e. [0025]: “… the asset data 114 can be generated by and/or associated with one or more assets, one or more devices, one or more machines and/or one or more types of equipment. ” and [0041]: “ The one or more assets 402.sub.1-N can also provide the asset data 114 to the database 404 via the network 406. Furthermore, the database 404 can store the asset data 114. ”) ; a data analysis subsystem (Fig .1, element 104 of Fig. 1, element 102; i.e. an asset digital twin modeling component of an asset performance component) configured to analyze the data collected by the data collection subsystem ( pg. 3, par. [0027] and pg. 10, par. [0057]; i.e. [0027]: “… to facilitate generation of the digital twin models, the digital twin modeling component 104 can perform learning with respect to the asset data 114. The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. ” and [0057]: “ At 1002, a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset is generated by a system comprising a processor (e.g., by digital twin modeling component 104). … For example, a digital twin model from the set of digital twin models can be generated based on one or more artificial intelligence techniques, one or more machine learning techniques and/or one or more other data analytics techniques associated with asset data for the asset. ”) ; and wherein the data collection subsystem and the data analysis subsystem are communicatively connected to each other through a network (pgs. 6-7, par. [0041] and Fig. 4, element 408 ; i.e. [0041]: “ In an aspect, the database 404 and the asset performance component 102 can be in communication via a network 408. The network 408 can be a communication network, a wireless network, an IP network, a voice over IP network, an internet telephony network, a mobile telecommunications network and/or another type of network. In an embodiment, the asset performance component 102 can receive the asset data 114 from the database 404. For example, the asset performance component 102 can receive the asset data 114 via the network 408.” ) , wherein the data analysis subsystem (Fig .1, element 104 of Fig. 1, element 102; i.e. the asset digital twin modeling component of the asset performance component) comprises: a data processing module (i.e. processor ) configured to process the data collected by the data collection subsystem (pg. 2, par. [0024] and pg. 7, par. [0042] and pg. 10, par. [0057] ; i.e. [0024]: “ The system 100 (e.g., the asset performance component 102) can further include a processor 112 to facilitate operation of the instructions (e.g., computer executable components and instructions) by the system 100 (e.g., the asset performance component 102). ” ; [0042]: “ Furthermore, the asset data 114 analyzed by the asset performance component 102 can be raw data and/or compressed data associated with the one or more assets 402.sub.1-N. Moreover, the asset performance component 102 can be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also analyzing the asset data 114. ”) ; a hybrid digital twin model ( i.e. a digital twin model) configured to process the data processed by the data processing module ( pg. 3, par. [002 6 ] and [0027] , pg. 7, par. [0042] and pg. 10, par. [0057] ; i.e. [002 6 ]: “Th e digital twin modeling component 104 can generate a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with the asset data 114. For instance, the digital twin modeling component 104 can generate a set of digital twin models indicative of a set of virtual representations for a set of physical components of an asset that is associated with the asset data 114. In an example, the digital twin modeling component 104 can generate a first digital twin model for a first physical component of an asset associated with the asset data 114, a second digital twin model for a second physical component of the asset associated with the asset data 114, a third digital twin model for a third physical component of the asset associated with the asset data 114, etc. ” ) while also analyzing the asset data 114. ” and [0057]: “ At 1002, a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset is generated by a system comprising a processor (e.g., by digital twin modeling component 104) . … For example, a digital twin model from the set of digital twin models can be generated based on one or more artificial intelligence techniques, one or more machine learning techniques and/or one or more other data analytics techniques associated with asset data for the asset . ” ) ; and a signal generation module (i.e. inference component ) configured to analyze the data processed by the hybrid digital twin model and output information signal ( pgs. 3-4, par. [002 6 ] - [0028] and pg. 7-8, par. [0046] ; i.e. [0026]: “ In another embodiment, a digital twin model from the set of digital twin models can provide approximate real-time information regarding information associated with fluid dynamics, thermal dynamics, combustion dynamics, aeromechanics, performance, operability and/or one or more other indicators of distress (e.g., part distress) for a physical component of an asset associated with the asset data 114. In an aspect, a digital twin model from the set of digital twin models can model progression of damage, progression of a degree of depreciation, progression of repair costs, progression of replacement costs and/or material demand for a physical component of an asset associated with the asset data 114. In an embodiment, the digital twin modeling component 104 can link a part number and/or a serial number for a physical component to a corresponding digital twin model from the set of digital twin models. ” and [0027]: “ The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate … generating inferences with respect to the asset data 114. ”) , and wherein the hybrid digital twin model is a fusion of an artificial intelligence learning and inference model (pg. 3, par. [0027]; i.e. “ The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. ” ) , which is based on installation operation data (pgs. 2-3, par. [0025]; i.e. operational data) , with a physical model regarding the one or more field installations (pgs. 7-8 , par. [0046] ; i.e. “ In an embodiment, the asset performance component 102 (e.g., the digital twin modeling component 104 of the asset performance component 102) can generate the set of digital twin models 606.sub.1-N based on the asset data 114 for the set of physical components 604.sub.1-N. ”) , and is trained based on installation operation data regarding the one or more field installations (pgs. 2-3, par. [0025] and pgs. 3-4, par. [0028]: “… the digital twin modeling component 104 can include an inference component that can further enhance automated aspects of the digital twin modeling component 104 utilizing in part inference based schemes to facilitate learning and/or generating inferences with respect to the asset data 114 and/or identifying similarity with one or more other assets (e.g., one or more other assets with a similar duty cycle, etc.). “ ) . Not explicitly taught are a control subsystem configured to control the one or more field installations, based on an output of the data analysis subsystem ; and wherein the data collection subsystem, the data analysis subsystem, and the control subsystem are communicatively connected to each other through a network . However Maturana, in an analogous art of hybrid data collection and analysis (abstract and pg. 1, par. [0001]), teaches the missing limitations of a control subsystem (Fig. 1, element 102; i.e. a cloud platform) configured to control one or more field installations, based on an output of data analysis subsystem ( pg. 3, par. [0034] and pg. 4, par. [0037], [0038], and [0040]; i.e. [0037]: “ An exemplary private cloud can comprise a set of servers hosting the cloud services 112 and residing on a corporate network protected by a firewall. ” and [0040]: “ Moreover, cloud based control applications can perform remote decision-making for a controlled industrial system based on data collected in the cloud from the industrial system, and issue control commands to the system via the edge device. These industrial cloud-computing applications are only intended to be exemplary, and the systems and methods described herein are not limited to these particular applications” ) ; and wherein a data collection subsystem (pgs. 4-5, par. [0042] and [0043] and Fig. 2, element 204 of Fig. 2, element 106; i.e. a collection services component of an edge device) , a data analysis subsystem (pgs. 4, par. [0042] and pg. 5, par. [0042] and Fig. 2, element 212 of Fig. 2, element 106; i.e. an edge analytics component of an edge device) , and the control subsystem (Fig. 1, element 102; i.e. the cloud platform) are communicatively connected to each other through a network (pg. 2, par. [0024] and Fig. 1, the arrow between “External or Private Network” and “Plant Level:; i.e. [0024]: “ The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal.”) for the purpose of controlling an industrial system (pg. 4, par. [0040]). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin to include the addition of the limitation s of a control subsystem configured to control one or more field installations, based on an output of data analysis subsystem; and wherein a data collection subsystem, a data analysis subsystem, and the control subsystem are communicatively connected to each other through a network to advantageously simplify integration of existing cloud-based data storage, analysis, or reporting applications used by an enterprise per automatic detection and communication with a cloud platform (Maturana: pg. 4, pg. [0040]). As per claim 2, Chapin teaches the installation operation data (Fig. 1, element 114 ; i.e. the asset data ) comprises field installation environment data (i.e. environmental data) and field installation management data (pgs. 2-3, par. [0025] ; i.e. “ The asset data 114 can include various data, such as but not limited to, sensor data, process data (e.g., process log data), operational data, monitoring data, maintenance data, parameter data, measurement data, performance data, industrial data, manufacturing data, machine data, asset data, equipment data, device data, meter data, real-time data, historical data, environmental data, audio data, image data, video data, and/or other data. ” ) . As per claim 3, Chapin teaches a model training subsystem (i.e. model training functionality of the asset performance component (Fig. 1, element 102)) configured to train the hybrid digital twin model, based on the installation operation data regarding the one or more field installations (pg. 3, par. [0027]; i.e. “… to facilitate generation of the digital twin models, the digital twin modeling component 104 can perform learning with respect to the asset data 114. The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. The digital twin modeling component 104 can perform learning with respect to the asset data 114 explicitly or implicitly. ”) . As per claim 5, Chapin does not expressly teach the data collection subsystem and the data analysis subsystem are arranged in an edge computing node, and wherein the control subsystem is arranged in a back-end computing node. However Maturana, in an analogous art of hybrid data collection and analysis (abstract and pg. 1, par. [0001]), teaches the missing limitations of the data collection subsystem (pgs. 4-5, par. [0042] and [0043] and Fig. 2, element 204 of Fig. 2, element 106; i.e. the collection services component of the edge device) and the data analysis subsystem (Fig. 2, element 212 of Fig. 2, element 106; i.e. the edge analytics component of the edge device) are arranged in an edge computing node (pgs. 4, par. [0042] and pg. 5, par. [0042] and [0043]), and wherein the control subsystem (Fig. 1, element 102; i.e. the cloud platform) is arranged in a back-end computing node (pg. 3, par. [0034] and pg. 4, par. [0037], [0038], and [0040]; i.e. [0037]: “ An exemplary private cloud can comprise a set of servers hosting the cloud services 112 and residing on a corporate network protected by a firewall. ” ) for the purpose of controlling an industrial system (pg. 4, par. [0040]). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin to include the addition of the limitation s of the data collection subsystem and the data analysis subsystem are arranged in an edge computing, and wherein the control subsystem is arranged in a back-end computing node to advantageously simplify integration of existing cloud-based data storage, analysis, or reporting applications used by an enterprise per automatic detection and communication with a cloud platform (Maturana: pg. 4, pg. [0040]). As per claim 7, Chapin does not expressly teach the back-end computing node is in a cloud sever. Maturana teaches the missing limitation of the back-end computing node is in a cloud server (pg. 3, par. [0034] and pg. 4, par. [0037], [0038], and [0040]; i.e. [0037]: “ An exemplary private cloud can comprise a set of servers hosting the cloud services 112 and residing on a corporate network protected by a firewall. ” ) for the purpose of controlling an industrial system (pg. 4, par. [0040]). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin to include the addition of the limitation of the back-end computing node is in a cloud server to advantageously simplify integration of existing cloud-based data storage, analysis, or reporting applications used by an enterprise per automatic detection and communication with a cloud platform (Maturana: pg. 4, pg. [0040]). As per claim 8, Chapin teaches the hybrid digital twin model comprises the same number of artificial intelligence learning and inference models as the number of types of the installation operation data (pgs. 2-3, par. [0025] and pgs. 6-7, par. [0046]; i.e. [0046]: “ In an embodiment, the asset performance component 102 (e.g., the digital twin modeling component 104 of the asset performance component 102) can generate the set of digital twin models 606.sub.1-N based on the asset data 114 for the set of physical components 604.sub.1-N. ”) . As per claim 11, Chapin substantially teaches the Applicant’s claimed invention. Chapin teaches the limitations of a n artificial intelligence based field installation control method for process operation optimization, the field installation control method comprising: collecting installation operation data (Fig. 4; element 114; i.e. asset data from one or more field installations (pgs. 2-3, par. [0025] and pgs. 6-7, par. [0041]; i.e. [0025]: “… the asset data 114 can be generated by and/or associated with one or more assets, one or more devices, one or more machines and/or one or more types of equipment. ” and [0041]: “ The one or more assets 402.sub.1-N can also provide the asset data 114 to the database 404 via the network 406. Furthermore, the database 404 can store the asset data 114. ”) ; analyzing the collected data; and controlling the one or more field installations, based on a result of the analyzing ( pg. 3, par. [0027] and pg. 10, par. [0057]; i.e. [0027]: “… to facilitate generation of the digital twin models, the digital twin modeling component 104 can perform learning with respect to the asset data 114. The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. ” and [0057]: “ At 1002, a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset is generated by a system comprising a processor (e.g., by digital twin modeling component 104). … For example, a digital twin model from the set of digital twin models can be generated based on one or more artificial intelligence techniques, one or more machine learning techniques and/or one or more other data analytics techniques associated with asset data for the asset. ”) , wherein the analyzing comprises: processing the collected data (pg. 2, par. [0024] and pg. 7, par. [0042] and pg. 10, par. [0057]; i.e. [0024]: “ The system 100 (e.g., the asset performance component 102) can further include a processor 112 to facilitate operation of the instructions (e.g., computer executable components and instructions) by the system 100 (e.g., the asset performance component 102). ”; [0042]: “ Furthermore, the asset data 114 analyzed by the asset performance component 102 can be raw data and/or compressed data associated with the one or more assets 402.sub.1-N. Moreover, the asset performance component 102 can be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also analyzing the asset data 114. ”) ; processing the processed data, through a hybrid digital twin model ( pg. 3, par. [0026] and [0027], pg. 7, par. [0042] and pg. 10, par. [0057]; i.e. a digital twin model; [0026]: “Th e digital twin modeling component 104 can generate a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with the asset data 114. For instance, the digital twin modeling component 104 can generate a set of digital twin models indicative of a set of virtual representations for a set of physical components of an asset that is associated with the asset data 114. In an example, the digital twin modeling component 104 can generate a first digital twin model for a first physical component of an asset associated with the asset data 114, a second digital twin model for a second physical component of the asset associated with the asset data 114, a third digital twin model for a third physical component of the asset associated with the asset data 114, etc. ” ) while also analyzing the asset data 114. ” and [0057]: “ At 1002, a set of digital twin models indicative of a set of virtual representations for a set of physical components associated with an asset is generated by a system comprising a processor (e.g., by digital twin modeling component 104) . … For example, a digital twin model from the set of digital twin models can be generated based on one or more artificial intelligence techniques, one or more machine learning techniques and/or one or more other data analytics techniques associated with asset data for the asset . ” ) ; generating information signal by analyzing the data processed by the hybrid digital twin model (pgs. 3-4, par. [0026]-[0028] and pg. 7-8, par. [0046]; i.e. [0026]: “ In another embodiment, a digital twin model from the set of digital twin models can provide approximate real-time information regarding information associated with fluid dynamics, thermal dynamics, combustion dynamics, aeromechanics, performance, operability and/or one or more other indicators of distress (e.g., part distress) for a physical component of an asset associated with the asset data 114. In an aspect, a digital twin model from the set of digital twin models can model progression of damage, progression of a degree of depreciation, progression of repair costs, progression of replacement costs and/or material demand for a physical component of an asset associated with the asset data 114. In an embodiment, the digital twin modeling component 104 can link a part number and/or a serial number for a physical component to a corresponding digital twin model from the set of digital twin models. ” and [0027]: “ The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate … generating inferences with respect to the asset data 114. ”), and wherein the hybrid digital twin model is a fusion of an artificial intelligence learning and inference model (pg. 3, par. [0027]; i.e. “ The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. ”), which is based on installation operation data (pgs. 2-3, par. [0025]; i.e. operational data), with a physical model regarding the one or more field installations (pgs. 7-8, par. [0046]; i.e. “ In an embodiment, the asset performance component 102 (e.g., the digital twin modeling component 104 of the asset performance component 102) can generate the set of digital twin models 606.sub.1-N based on the asset data 114 for the set of physical components 604.sub.1-N. ”), and is trained based on installation operation data regarding the one or more field installations (pgs. 2-3, par. [0025] and pgs. 3-4, par. [0028]: “… the digital twin modeling component 104 can include an inference component that can further enhance automated aspects of the digital twin modeling component 104 utilizing in part inference based schemes to facilitate learning and/or generating inferences with respect to the asset data 114 and/or identifying similarity with one or more other assets (e.g., one or more other assets with a similar duty cycle, etc.). ” ). Not explicitly taught is generating a control information signal by analyzing the processed data . However Maturana, in an analogous art of hybrid data collection and analysis (abstract and pg. 1, par. [0001]), teaches the missing limitation of generating control information signal by analyzing processed data (pg. 3, par. [0034], pg. 4, par. [0037], [0038], and [0040]; i.e. [0037]: “ An exemplary private cloud can comprise a set of servers hosting the cloud services 112 and residing on a corporate network protected by a firewall. ”; [0040]: “ Moreover, cloud based control applications can perform remote decision-making for a controlled industrial system based on data collected in the cloud from the industrial system, and issue control commands to the system via the edge device. These industrial cloud-computing applications are only intended to be exemplary, and the systems and methods described herein are not limited to these particular applications.”; and [0045]: “ Edge analytics component 212 can be configured to perform edge-level analytics on selected subsets of the collected industrial data. In some embodiments, edge analytics component 212 can be configured to work in conjunction with queue processing component 206 to facilitate sending selected results of the edge-level analytics, as well as any relevant industrial data, to the cloud platform for storage or cloud-level analytics. ”) for the purpose of controlling an industrial system (pg. 4, par. [0040]). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin to include the addition of the limitation of generating control information signal by analyzing processed data to advantageously simplify integration of existing cloud-based data storage, analysis, or reporting applications used by an enterprise per automatic detection and communication with a cloud platform (Maturana: pg. 4, pg. [0040]). As per claim 12 , Chapin in view of Maturana teaches a non-transitory computer-readable recording medium for storing instructions, when executed by one or more processors, configured to perform the method of claim 11 ( Chapin: pg. 11, par. [0065] and pg. 13, par. [0076]-[0078] and claim 12 stands rejected for the same rationale as set forth in claim 11 by virtue of incorporation of “the method of claim 11” into claim 12) . Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin in view of Maturana in further view of U.S. Patent Publication No. 2023/0166211 A1 (hereinafter Hazui) . As per claim 4 , Chapin view of Maturana does not expressly teach the artificial intelligence learning and inference model comprises: an input layer; an output layer; and one or more hidden layers between the input layer and the output layer, and wherein at least some of the one or more hidden layers comprise nodes implementing the physical model. However Hazui, in an analogous art of modeling (pg. 2, par. [0021]), teaches the missing limitations of a model (i.e. a neural network) comprises: an input layer (pg. 3, par. [0030 and Fig. 1, element 12); an output layer (pg. 3, par. [0030 and Fig. 1, element 14); and one or more hidden layers between the input layer and the output layer (pg. 3, par. [0030 and Fig. 1, element 16) , and wherein at least some of the one or more hidden layers comprise nodes implementing a physical model (pg. 2, par. [0023] and pg. 3, par. [0030]; i.e. an intermediate layer (hidden layer) includes a plurality of nodes) for the purpose of calculating equipment data (pg. 2, par. [0021]) . Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin view of Maturana to include the addition of the limitation s of a model comprises: an input layer; an output layer; and one or more hidden layers between the input layer and the output layer, and wherein at least some of the one or more hidden layers comprise nodes implementing a physical model to advantageously improve prediction/estimation accuracy ( Hazui : pg. 3 , pg. [00 31 ]). Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chapin in view of Maturana in further view of U.S. Patent Publication No. 2020/0218937 A1 (hereinafter Scarzanella). As per claim 6, Chapin teaches t he physical model fused into the hybrid digital twin model (pg. 3, par. [0027]; i.e. “ The digital twin modeling component 104 can also generate inferences with respect to the asset data 114. The digital twin modeling component 104 can, for example, employ principles of artificial intelligence to facilitate learning and/or generating inferences with respect to the asset data 114. ”). Chapin does not expressly teach the physical model regarding the one or more field installations is arranged in the back-end computing node, wherein the physical model fused into the hybrid digital twin model is configured to be obtained by loading the physical model arranged in the back-end computing node through the network. However Maturana, in an analogous art of hybrid data collection and analysis (abstract and pg. 1, par. [0001]), teaches the missing limitation of a physical model regarding one or more field installations is arranged in the back-end computing node (pg. 4, par. [0038] ; i.e. [ 0038] : “ Cloud platform 102 may also include one or more object models to facilitate data ingestion and processing in the cloud. ”) for the purpose of controlling an industrial system (pg. 4, par. [0040]). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Chapin to include the addition of the limitation a physical model regarding one or more field installations is arranged in the back-end computing node to advantageously simplify integration of existing cloud-based data storage, analysis, or reporting applications used by an enterprise per automatic detection and communication with a cloud platform (Maturana: pg. 4, pg. [0040]). Chapin in view of Maturana does not expressly teach the physical model fused into the hybrid digital twin model is configured to be obtained by loading the physical model arranged in the back-end computing node through the network. However Scarzanella, in an analogous art of training a model (pg. 1, par. [0001]), teaches the missing limitation of a mode is configured to be obtained by loading the model arranged in a back-end computing node through a network (pg. 4, par. [0056] and [0057]; i.