CTNF 17/782,678 CTNF 99207 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-187 AIA 12-239 In view of the appeal brief filed on 03/13/2026 , PROSECUTION IS HEREBY REOPENED. New grounds of rejection are set forth below. To avoid abandonment of the application, appellant must exercise one of the following two options: (1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or, (2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid. A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below: /MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129 Response To Arguments Remarks pages 6-12, Applicant contends: Application should not be rejected under 101. Response: Claim 1 is not considered rejected under 101, as a result any rejections directed towards claim 1 under 101 are not considered persuasive. Dependent claims can add material that results in the claim being rejectable under 101, as the MPEP notes with emphasis added: ([MPEP 2106.07]: “When evaluating a claimed invention for compliance with the substantive law on eligibility, examiners should review the record as a whole (e.g., the specification, claims, the prosecution history, and any relevant case law precedent or prior art) before reaching a conclusion with regard to whether the claimed invention sets forth patent eligible subject matter. The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application. For example, even if an independent claim is determined to be ineligible, the dependent claims may be eligible because they add limitations that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception recited in the independent claim. And conversely, even if an independent claim is determined to be eligible, a dependent claim may be ineligible because it adds a judicial exception without also adding limitations that integrate the judicial exception or provide significantly more. Thus, each claim in an application should be considered separately based on the particular elements recited therein.”). Claims 6-9 being rolled up, may further specify aspects of the application, butclaims 6-9 claims did not integrate the application into a practical application or satisfy 101 for the 101 rejections, as claim 1 was not rejected under 101, but was listed as having additional elements. The rejected claims (some dependents under claim 1) introduced abstract ideas that were not integrated into practical applications by the additional elements. Claims 6-9 were claims that did not contain abstract ideas, so no rejection was made as no abstract idea was present. The data structure of a graph like a knowledge graph is not seen as matching the idea in Enfish, as the claims do not appear to indicate improved computer functionality beyond what is noted in the current 101 rejections (which the limitations are noted as being additional elements that do not integrate into a practical application, such as apply it, or abstract ideas). Noting further with an example, the application of machine learning, such as recited in claim 1, is seen as apply it as the claim reads “applying machine learning to the CEG storing the received information…” which is simply applying machine learning in a generic manner to information “for representing the knowledge assisting a user of the CES…”. The functional manner of the claim, as well as the lack of particular details on the applying of the machine learning is why the claim is interpreted as apply it (MPEP 2106.05(f): "The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743."). Elements for data storage are seen as storing data, for the data representing other types or objects (even objects in industrial engineering) does not prevent the data from being stored data used for some process (see MPEP 2106.05(d)(iv) for a computer), as a result the storing of graphs is not seen as unconventional. Currently recited art in rejection of claim 1 (Martinez teaches graphs of nodes and edges representing things as indicated in claim 1) notes the storage of elements in graphs, thus the idea being unconventional does not appear to be indicated by prior art. Claim 12 recites a programming module, but the programming module was not argued to be an abstract performable in the human mind. The limitation directed to the programming module was noted as being “apply it”. As a result, the arguments directed to abstract idea related to the programming module are not seen as persuasive. Claim 12 was rejected for containing abstract idea as the claims indicate elements of identifying elements (such as “a knowledge extraction module for identifying… from data received from a physical automation system”). Generically identifying data, especially data that is not seen as being recited in a manner that is not human readable, is seen as a process performable in a human mind (MPEP 2106.04(Section 3): "The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same)."). The other limitation in claim 12 of “ a machine learning module for analyzing knowledge extracted by the knowledge extraction module and identifying characteristics of the automation system ” is seen as analyzing knowledge and identifying characteristics, which are seen as abstract ideas performable in the human mind. The indication of a machine learning module performing the steps is seen as an implementation of the abstract idea in a computer (MPEP 2106.04(a)(2)(3): “An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that ‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper’.”). The knowledge representation in claim 12 is noted as being a continuation of abstract ideas, as the knowledge representation is not seen as directing away from the abstract ideas, as indicating the knowledge is represented using nodes and edges does not prevent the interpretation of mental processes. Providing more detail on how the applications of elements are being applied could result in the elements being a particular and practical implementation, but the “apply it” limitations as recited are interpreted as broader or generic recitations. As a result, the arguments in regards to 101 rejections are not seen as persuasive, thus the 101 rejections remain. Remarks pages 13-19, Applicant contends: Martinez fails to properly teach elements taught by the claimed limitations. Martinez does not teach “storing a plurality of previously generated CEGs representative of other prior automation engineering projects”. Response: Arguments in regards to Martinez and the teachings of Martinez are seen as moot (aside from the argument regarding Martinez teaching a plurality of previously generated CEGs which is addressed below), as the arguments are in regards to elements that have updated teachings or art (such as PLC, HMI, automation engineering control systems, etc), thus the arguments are not seen as pertaining to the current rejections. As noted in previous office action responses, Martinez does teach “storing a plurality of previously generated CEGs representative of other prior automation engineering projects” as shown in the claim rejection for current claim 1 (Figure 6 609 shows historical data of graphs). The interpretation of the snapshots counting as projects come from the specification of Martinez noting that the “snapshots” can be perceived as a series of knowledge graphs ([Martinez 0033]: “The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.). This abstraction 120 will enable: a) a user to define custom queries; b) interactions with various machine learning (ML) tools; c) interactions to facilitate autonomous CPS functions; and d) interactions with databases. Using this language abstraction 120, various ML tools such as reinforcement learning 160, generative adversarial networks 161, and deep learning 162, along with other ML methods 163 may be utilized to create what may be called a "Cognitive CPS".”). This means under BRI, Martinez is seen as teaching the required limitation related to graphs for previous projects. The noted argument related to “Martinez does not teach the storing and comparing of discrete prior automation projects via CEG” as the word “discrete” is not present in the claims, thus does not affect the claim limitations. MPEP 2106.05(a): "After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100." The word “other” is not seen as meaning “discrete and separate”. The word “other” is seen as conveying multiple, but no indication of any particular relation of the multiple elements. Thus the claim limitations related to the use of historical or previous project graphs are still seen as taught by Martinez under the corresponding limitation mapping within the claims, for Martinez is still seen as teaching the historical or previous project graphs. The current rejections also contain further support for historical elements using Baier for further information and support of how other art notes the use of historical elements or data as input, such as for machine learning models. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 3-5, 10-17, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. In regards to Claim 1 (not rejected but listed for clarity): Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter? Yes, it is directed towards a method, so a process. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 1 recites the following additional elements: receiving information relating to an automation engineering project from an engineering tool This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). storing the received information in a cognitive engineering graph (CEG) comprising a plurality of nodes representative of physical objects of an automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes, wherein the CEG includes at least one node that represents a human machine interface (HMI), and/or at least one node that represents a programmable logic controller (PLC) At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). storing a plurality of previously generated CEGs representative of other prior automation engineering projects At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). applying machine learning to the CEG storing the received information and the stored plurality of previously generated CEGs for representing the knowledge assisting a user of the CES during design of the automation system in the automation engineering project At a high level of generality, this is an activity of applying machine learning to the CEG as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 1 recites the following additional elements: receiving information relating to an automation engineering project from an engineering tool This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). storing the received information in a cognitive engineering graph (CEG) comprising a plurality of nodes representative of physical objects of an automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes, wherein the CEG includes at least one node that represents a human machine interface (HMI), and/or at least one node that represents a programmable logic controller (PLC) At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). Storing data to a process in memory does not add a meaningful limitation to the process of generating a first client model. At a high level of generality this is a well-understood, routine, conventional activity (see MPEP 2106.05(d)(iv) for a computer). Storing data in memory is a well-understood, routine, conventional activity in the field of computers and computer science. The addition of noting more specifically what the nodes could be representing does not change the limitation from being more than an act of storing information. storing a plurality of previously generated CEGs representative of other prior automation engineering projects At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). Storing data to a process in memory does not add a meaningful limitation to the process of generating a first client model. At a high level of generality this is a well-understood, routine, conventional activity (see MPEP 2106.05(d)(iv) for a computer). Storing data in memory is a well-understood, routine, conventional activity in the field of computers and computer science. establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as communication (see MPEP 2106.05(h)(x)), do not amount to significantly more than the exception itself. applying machine learning to the CEG storing the received information and the stored plurality of previously generated CEGs for representing the knowledge assisting a user of the CES during design of the automation system in the automation engineering project At a high level of generality, this is an activity of applying machine learning to the CEG as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “applying machine learning to the CEG” does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regard to claim 3: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 3 recites the following abstract ideas: analyzing the CEG storing the received information to identify at least one pattern that is representative of a given object of interest from the automation engineering project This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. In regard to claim 4: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 4 recites the following abstract ideas: automatically by the CES, adding an element to the CEG storing the received information and on a query from a user This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. In regard to claim 5: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 5 recites the following abstract ideas: performing an undo action by the CES at a request of the same or a different user to remove the element that was automatically added to the CEG This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. An undo action is interpreted here as a form of removal of something based on the claim limitation. Removing something, even a node from a graph, is an abstract idea capable of being performed in the human mind or with pen and paper, as a human could cross or erase a node from a written graph. In regard to claim 10: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 10 recites the following abstract ideas: comparing the CEG storing the received information and the stored plurality of previously generated CEGs This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. validating a design for the automation engineering project based on the comparison This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as judgement. In regard to claim 11: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 11 recites the following abstract ideas: comparing the CEG storing the received information and the stored plurality of previously generated CEGs This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. determining a proposed course of action for a user to perform in the automation engineering project based on the comparison This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 11 recites the following additional elements: communicating the propose course of action to the user This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 11 recites the following additional elements: communicating the propose course of action to the user This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regard to claim 12: Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter? Yes, it is directed towards a system, so a machine. Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 12 recites the following abstract ideas: a cognitive system in communication with the computer-based engineering tool comprising: a knowledge extraction module for identifying This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. a machine learning module for analyzing knowledge extracted by the knowledge extraction module and identifying characteristics of the automation system This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. the knowledge representation comprising a cognitive engineering graph (CEG), the CEG comprising a plurality of nodes representative of physical objects of the physical automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes, wherein the CEG includes at least one node that represents a human machine interface (HMI), and/or at least one node that represents a programmable logic controller (PLC) This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from this claim (claim 12). Having a knowledge representation does not integrate the abstract idea into a practical application. This claim limitation does not add anything that would change the interpretation of the abstract ideas. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 12 recites the following additional elements: a computer-based engineering tool for providing at least one of designing, programming simulation and testing of an automation system This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). At a high level of generality, this is an activity of using a “computer-based” as an “apply it” use (see MPEP 2106.05(f)). storing information contained in an automation engineering project of the computer-based engineering tool At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). and from data received from a physical automation system This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). an inductive programming module for automatically generating control programs for the automation system based on the stored information from the knowledge extraction module At a high level of generality, this is an activity of using training samples and a loss function as an “apply it” use (see MPEP 2106.05(f)). a computer memory storing a plurality of CEGs from previously designed projects in communication with the machine learning module for analyzing past knowledge At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 12 recites the following additional elements: a computer-based engineering tool for providing at least one of designing, programming simulation and testing of an automation system This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as engineering tool for an automation system (see MPEP 2106.05(h)(x)), do not amount to significantly more than the exception itself. At a high level of generality, this is an activity of using a “computer-based” as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a “computer-based” appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea. storing information contained in an automation engineering project of the computer-based engineering tool At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). Storing data to a process in memory does not add a meaningful limitation to the process of generating a first client model. At a high level of generality this is a well-understood, routine, conventional activity (see MPEP 2106.05(d)(iv) for a computer). Storing data in memory is a well-understood, routine, conventional activity in the field of computers and computer science. and from data received from a physical automation system This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). an inductive programming module for automatically generating control programs for the automation system based on the stored information from the knowledge extraction module At a high level of generality, this is an activity of using training samples and a loss function as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “ inductive programming module of automatically generating control programs for the automation system ” using “ stored information from the knowledge extraction module ” does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. a computer memory storing a plurality of CEGs from previously designed projects in communication with the machine learning module for analyzing past knowledge At a high level of generality, this is an Insignificant extra-solution activity (MPEP 2106.05(g) for Mere Data Gathering). Storing data to a process in memory does not add a meaningful limitation to the process of generating a first client model. At a high level of generality this is a well-understood, routine, conventional activity (see MPEP 2106.05(d)(iv) for a computer). Storing data in memory is a well-understood, routine, conventional activity in the field of computers and computer science. a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regard to claim 15: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 15 recites the following additional elements: wherein the feedback module is configured to provide the user with a design recommendation for the automation engineering project based on an output from the machine learning module This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Ste p 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 15 recites the following additional elements: wherein the feedback module is configured to provide the user with a design recommendation for the automation engineering project based on an output from the machine learning module This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regard to claim 16: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 16 recites the following additional elements: a communication channel between the physical automation system and the knowledge extraction module for extracting operations data from the automation system for analysis by the cognitive system This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Ste p 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 16 recites the following additional elements: a communication channel between a physical automation system and the knowledge extraction module for extracting operations data from the automation system for analysis by the cognitive system This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as communication (see MPEP 2106.05(h)(x)), do not amount to significantly more than the exception itself. In regard to claim 17: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 17 recites the following abstract ideas: an automated reasoning module in communication with the knowledge representation and the machine learning module This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 12. the automated reasoning module configured to automatically add a component to the automation engineering project based on the knowledge representation and the machine learning module This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here it is seen as evaluation. In regard to claim 21: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 21 recites the following abstract ideas: analyzing the plurality of previously generated CEGs to discover at least one useful pattern that identify certain objects or processes for use in the future This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as observation and evaluation. In regard to claim 22 (not rejected but listed for clarity): Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 22 recites the following additional elements: wherein the automation project comprises components that define functionality for the HMI, wherein the HMI provides interaction between the automation system with a human user or operator This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Ste p 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 22 recites the following additional elements: wherein the automation project comprises components that define functionality for the HMI, wherein the HMI provides interaction between the automation system with a human user or operator This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). Defining functionality for a system that provides interaction with a user is seen as a form of input. In regard to claim 23 (not rejected but listed for clarity): Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 23 recites the following additional elements: wherein the automation project provides functionality for the PLC for control of the automation system, wherein the PLC monitors operation of the automation system and provides control of the system through various signals At a high level of generality, this is an activity of using a PLC as an “apply it” use (see MPEP 2106.05(f)). Ste p 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 23 recites the following additional elements: wherein the automation project provides functionality for the PLC for control of the automation system, wherein the PLC monitors operation of the automation system and provides control of the system through various signals At a high level of generality, this is an activity of using a PLC as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, using a PLC as a component to monitor and control does not integrate the abstract idea into a practical application, as the PLC appears to be merely a tool and thus is seen as a variation of the phrase “apply it”. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claims 1, 3, 4, 10, 11, 21, 22, 23 i s/are rejected under 35 U.S.C. 103 as being unpatentable over M artinez Canedo et al (WO 2018140365 A1), referred to as Martinez in this document, and further in view of Eller et al (US 6643555), referred to as Eller in this document, and further in view of Baier et al (US 7930639), referred to as Baier in this document. R egarding Claim 1: Martinez teaches: A method for representing knowledge in a cognitive engineering system (CES) comprising: receiving information relating to an automation engineering project from an engineering tool; [Martinez 0006]: “According to an embodiment, the method further comprises recording a plurality of user actions in the at least one user tool [A method for representing knowledge in a cognitive engineering system (CES) comprising: receiving information relating to an automation engineering project from an engineering tool] , storing the plurality of user actions in chronological order to create a series of user actions, and storing historical data relating a plurality of stored series of user actions.” Support for particular elements, such as PLC and HMI (which are interpreted as elements of an engineering) are shown later by Eller. Martinez is seen as teaching elements for automation engineering (title of Martinez is “System and method for cognitive engineering technology for automation and control of systems”) and engineering tool (as noted to be related to engineering and shows a user tool, thus a user is seen as being able to be an engineer), but support in relation to industrial automation is shown later in teachings from Eller. storing the received information in a cognitive engineering graph (CEG) comprising a plurality of nodes representative of physical objects of an automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes [Martinez 0033]: “Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) [storing the received information in a cognitive engineering graph (CEG) comprising a plurality of nodes representative of physical objects of an automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes] will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102.” physical objects of an automation system in the automation engineering project [Martinez 0015]: “A system for cognitive engineering according to aspects of embodiments of this disclosure comprise a database for extracting and storing user actions in at least one user tool, a cyber-physical system (CPS) comprising at least one physical component [physical objects of an automation system in the automation engineering project] , a computer processor in communication with the database and the at least one physical component configured to construct a digital twin graph representative of the CPS, and at least one machine learning technique, executable by the computer processor and configured to generate at least one engineering option of the CPS.” Further support is shown in the combination with Eller as shown later, as Eller teaches physical and non-physical elements (such as in Figure 6). and storing a plurality of previously generated CEGs representative of other prior automation engineering projects; [Martinez Figure 6] PNG media_image1.png 452 656 media_image1.png Greyscale Figure 6 609 shows historical data of graphs [and storing a plurality of previously generated CEGs representative of other prior automation engineering projects] . Further support of storing historical data related to industrial systems for use by machine learning or artificial intelligence is taught by Baier. and establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs [Martinez 0015]: “a computer processor in communication [and establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs] with the database and the at least one physical component configured to construct a digital twin graph representative of the CPS” The above is seen as teaching that a communication between stored data, such as previous graphs, and other elements received. applying machine learning to the CEG storing the received information and the stored plurality of previously generated CEGs for representing the knowledge assisting a user of the CES during design of the automation system in the automation engineering project [Martinez 0005]: “According to aspects of embodiments of the present invention, a method of performing cognitive engineering comprises, extracting human knowledge from at least one user tool, receiving system information from a cyber-physical system (CPS), organizing the human knowledge and the received system information into a digital twin graph (DTG), performing one or more machine learning techniques [applying machine learning to the CEG storing the received information and the stored plurality of previously generated CEGs for representing the knowledge assisting a user of the CES during design of the automation system in the automation engineering project] on the DTG to generate an engineering option relating to the CPS , and providing the generated engineering option to a user in the at least one user tool.” Martinez does not explicitly teach: Or an automation program for controlling a corresponding physical object in the automation engineering project Martinez teaches elements of software that controls physical objects [Martinez 0048], but the specifics of an automation program for an automation engineering project are seen as better taught by Eller. wherein the CEG includes at least one node that represents a human machine interface (HMI) Martinez teaches the use of objects that can act as a human machine interface being a part of nodes, such as IoT devices in [Martinez 0039]. However the specific teaching of a HMI being a hardware equipment is better taught by Eller. and/or at least one node that represents a programmable logic controller (PLC) Martinez teaches PLCs and appears to indicate such elements are a part of the network of nodes, such as in [Martinez 0003]: “Cyber-Physical Systems (CPSs) components such as Programmable Logic Controllers (PLC) are programmed to do a specific task, but they are incapable of achieving self-awareness. Moreover, current CPSs lack the capability of artificial intelligence (Al).”. To better teach PLC as an element is better taught in Eller. Eller teaches: Or an automation program for controlling a corresponding physical object in the automation engineering project [Eller Specification Technical Field Column 1 page 1]: “The present invention relates generally to an industrial automation system including software that is used to collect data, to monitor devices within an industrial environment and to trend characteristics of devices within an industrial environment for monitoring and/or controlling the industrial environment or control structure. More specifically, the present invention relates to automatically generating an application [Or an automation program for controlling a corresponding physical object in the automation engineering project] of a defined process for a control system.” wherein the CEG includes at least one node that represents a human machine interface (HMI) and/or at least one node that represents a programmable logic controller (PLC) PNG media_image2.png 1072 1365 media_image2.png Greyscale Figure 6 of Eller displays that models of the engineering system include elements such as PLC (top of Topological Model (30) [and/or at least one node that represents a programmable logic controller (PLC)] ) and HMI (middle of Topological Model (30) [wherein the CEG includes at least one node that represents a human machine interface (HMI)] ) One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Martinez and Eller. Martinez and Eller are in the same field of endeavor of system automation and engineering. One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Martinez and Eller to incorporate industrial engineering elements such as PLC, HMI, and physical industrial elements, as Martinez notes that elements of machine learning can be used to enhance systems ([Martinez 0004]: “Currently, there are attempts to integrate Al into CPS. For example, recent research showed that PLCs and edge devices, such as smart sensors, can be programmed using Al techniques to achieve new capabilities.”) and the use in the industrial engineering environment would allow industrial engineering systems to benefit from the capabilities machine learning can offer. Martinez already notes relatable aspects, such as PLCs (as noted in Martinez 0004), thus the combination is mostly an indication of elements of industrial engineering being modeled elements, such as in a graph. Eller, as shown in figure 6, displays that elements of industrial engineering like PLCs, HMIs, and physical components are elements already being modeled, thus the teachings of Martinez modelling a system using a graph on an industrial engineering system would reasonably contain the elements of the industrial engineering system. Aspects of Eller also support the idea of being related to a tool for the system ([Eller Column 3 near line 9]: “The present invention can be implemented as a single, integrated tool, which will allow the user to better analyze and understand his process needs and to develop process objects specific to these needs. The process objects encapsulate the principle parts of an automated machine in objects.”). Baier alternatively teaches: and storing a plurality of previously generated CEGs representative of other prior automation engineering projects [Baier Column 12 Line 10]: “Typically, the system 1000 can be viewed as a Distributed Historian [and storing a plurality of previously generated CEGs representative of other prior automation engineering projects] that spans machines, plants, and enterprises. At level 1030, the historian collects data at the rack level and is coupled to Common Plant Data Structure described above. Such can include collecting process & discrete data, alarms & events in a single archive if desired.” Historians are noted as being elements that stores data for use of correlations ([Baier Column 2 Line 27]: “The subject innovation provides for a historian(s) with a correlation component(s) that discovers relations and correlates among disparate pieces of data, to infer possible relationships between historian data/events and the industrial process (e.g., predict an outcome thereof). The correlation component can employ a heuristic model to capture process data/event data, and further include an implicit correlation component and an explicit correlation component. The explicit correlation component can employ predetermined models that are set by a user/external data sources, and the implicit correlation component can deduce relations among causes of triggering events (e.g., dynamically and/or in real-time during operation). For example, instead of merely storing values, messages that cause transition of values can be stored and compared via the implicit correlation component to derive correlations among various states that share the same messages. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process.”) The relation to AI is shown in ([Baier Column 6 Line 56]: “In a related aspect, the inference component 610 can further employ an artificial intelligence (AI) component to facilitate correlating outcome of an industrial process with historian data and/or events.”) One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Martinez and Baier. Martinez and Baier are of the same field of endeavor of machine learning and automation. One of ordinary skill in the art would have been motivated to combine Martinez and Baier in order to utilize historical information for automation processes, such as information from other systems, in order to both keep data for records for regulation ([Baier Column 1 Line 50]: “Another requirement of modern control system architectures is the ability to record and store data in order to maintain compliance with administrative regulations.”) and to increase quality in an automation environment ([Baier Column 2 Line 46]: "In accordance to a related methodology of the subject innovation, initially a set of data related to the industrial process can be collected. Such data can then be correlated to a predetermined model and a model that best fits (e.g., statistically) can subsequently be selected. Accordingly, quality analysis can occur ahead of processing and during the control process via employing historian data at various granularity levels... Moreover, based on such historian data, the quality control process of the subject innovation can predict outcome of quality for the industrial process, and initiate correction actions in view of current values of data... By associating historians with quality procedures, timely, tighter and more stringent controls can be applied to various automation processes—thus increasing overall quality in an automated manufacturing environment."). Thus to one of ordinary skill in the art, the keeping of historical data for use in an automation system, such as by a machine learning model, is obvious. Regarding Claim 3: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez teaches: analyzing the CEG storing the received information to identify at least one pattern that is representative of a given object of interest from the automation engineering project [Martinez 0040]: “The DTG 101 is dynamic in the sense that the graph is continuously evolving with the creation and elimination of nodes 203 and edges 201. This is because the DTG 101 is continuously updated by data, queries, simulation, models, new providers, new consumers, and dynamic relationships between them. Even though the DTG 101 may consist of a large graph with billions of nodes 203 and edges 201 , existing databases (e.g., GraphX, Linked Data) and algorithms (e.g. Pregel, MapReduce) running in cloud platforms may help to efficiently search and update the DTG 101. The DTG 101 representation is also suitable for a smooth integration with novel mathematical engines based on graph-theoretic and categorical approaches. The constant spatio-temporal evolution of the DTG 101 is captured in terms of a time-series of snapshots. The current snapshot of the DTG 101 reports the status of the operational environment (OE) and the OE's components such as CPS. Snapshots in the past provide a historical perspective that can be used to identify known patterns [analyzing the CEG storing the received information to identify at least one pattern that is representative of a given object of interest from the automation engineering project] with supervised learning, and unknown patterns with unsupervised learning. After these learned models are created, the DTG 101 can also be used to predict outcomes.” “a given object of interest” is seen a form of user input under BRI, which user input is taught in [Martinez 0055]: “A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device”. Evidence the pattern can contain objects is given by paragraph 37 noting that the graph contains objects (objects and other aspects of relation to industrial engineering are taught by the teachings in Claim 1) and associated relations [Martinez 0037]: “A Digital Twin is a living digital representation of an object that co-evolves with the real object. Every object, and the interactions and interrelationships between objects are maintained in a web of linked-data sets referred to as the Digital Twin Graph (DTG).” Regarding Claim 4: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez teaches: automatically by the CES, adding an element to the CEG storing the received information based on a query from a user [Martinez 0011]: “The DTG may change over time through at least one of the following: an addition of a node [automatically by the CES, adding an element to the CEG storing the received information based on a query from a user] ; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time creates a causal dependency that may be used by the one or more machine learning techniques to generate the engineering option.” Further support that steps can be automatic [Martinez 0065]: “The functions and process steps herein may be performed automatically or wholly or partially in response to user command. An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity”. User interaction for a query from the user is further supported by [Martinez 0055]: “A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device” Regarding Claim 10: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez teaches: comparing the CEG storing the received information and the stored plurality of previously generated CEGs; and validating a design for the automation engineering project based on the comparison [Martinez 0011]: “The DTG may change over time through at least one of the following: an addition of a node; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time [comparing the CEG storing the received information and the stored plurality of previously generated CEGs; as the changes here are seen as difference between the current and past DTG, thus is a comparison ] creates a causal dependency that may be used by the one or more machine learning techniques to generate the engineering option. [and validating a design for the automation engineering project based on the comparison] ” Support for the changes in DTG being a comparison of graphs [Martinez 0033]: “Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102.” Support for the engineering option being able to validate a design is given by paragraph 49 noting insight and requirements [Martinez 0049]: “FIG. 4 is an illustration of a potential benefit of PiU. According to one non- limiting example, CENTAUR can parse through millions of pictures and videos of people having a barbecue. After labeling, millions of forks 401 and spatulas 403 are identified as utensils commonly used in barbecues. This knowledge in the form of PiU data streams 405, are represented in the DTG 407, may be used by Deep Learning 409 and inference algorithms 41 1 to generate insights and requirements for a potential new product 413, the "spork", that combines the functionalities of both in a single utensil. Further PiU data 417 may be generated as the new product 413 is used and provided to update the DTG 407. Combined with the EaW, CENTAUR can then suggest the idea to the designers 415, and guide them step by step through the engineering process of the new product. The goal is to produce novel, useful, non-obvious products in a fraction of the time compared to the current product design practices.” Regarding Claim 11: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez teaches: comparing the CEG storing the received information and the stored plurality of previously generated CEGs; and determining a proposed course of action for a user to perform in the automation engineering project based on the comparison; [Martinez 0011]: “The DTG may change over time through at least one of the following: an addition of a node; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time [comparing the CEG storing the received information and the stored plurality of previously generated CEGs as the changes here are seen as difference between the current and past DTG, thus is a comparison ] creates a causal dependency that may be used by the one or more machine learning techniques to generate the engineering option. [and determining a proposed course of action for a user to perform in the automation engineering project based on the comparison] ” and communicating the propose course of action to the user [Martinez 0049]: “FIG. 4 is an illustration of a potential benefit of PiU. According to one non- limiting example, CENTAUR can parse through millions of pictures and videos of people having a barbecue. After labeling, millions of forks 401 and spatulas 403 are identified as utensils commonly used in barbecues. This knowledge in the form of PiU data streams 405, are represented in the DTG 407, may be used by Deep Learning 409 and inference algorithms 41 1 to generate insights and requirements for a potential new product 413, the "spork", that combines the functionalities of both in a single utensil. Further PiU data 417 may be generated as the new product 413 is used and provided to update the DTG 407. Combined with the EaW, CENTAUR can then suggest the idea to the designers [and communicating the propose course of action to the user] 415, and guide them step by step through the engineering process of the new product. The goal is to produce novel, useful, non-obvious products in a fraction of the time compared to the current product design practices.” Regarding claim 21: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez teaches: analyzing the plurality of previously generated CEGs to discover at least one useful pattern that identify certain objects or processes for use in the future [Martinez 0040]: “The DTG 101 is dynamic in the sense that the graph is continuously evolving with the creation and elimination of nodes 203 and edges 201. This is because the DTG 101 is continuously updated by data, queries, simulation, models, new providers, new consumers, and dynamic relationships between them. Even though the DTG 101 may consist of a large graph with billions of nodes 203 and edges 201 , existing databases (e.g., GraphX, Linked Data) and algorithms (e.g. Pregel, MapReduce) running in cloud platforms may help to efficiently search and update the DTG 101. The DTG 101 representation is also suitable for a smooth integration with novel mathematical engines based on graph-theoretic and categorical approaches. The constant spatio-temporal evolution of the DTG 101 is captured in terms of a time-series of snapshots. The current snapshot of the DTG 101 reports the status of the operational environment (OE) and the OE's components such as CPS. Snapshots in the past provide a historical perspective that can be used to identify known patterns [analyzing the plurality of previously generated CEGs to discover at least one useful pattern that identify certain objects or processes for use in the future] with supervised learning, and unknown patterns with unsupervised learning. After these learned models are created, the DTG 101 can also be used to predict outcomes.” The use of historical elements being utilized is also explained in the teachings of Claim 1. Regarding claim 22: The method of claim 1 is taught by Martinez, Eller, and Baier. Martinez notes aspects related to human interaction components, such as in [Martinez 0044], but Eller is used to better teach HMI. Eller teaches: wherein the automation project comprises components that define functionality for the HMI, wherein the HMI provides interaction between the automation system with a human user or operator [Eller Column 5 near line 10]: “Preferably, the application generator 20 generates controller logic for Concept, a control programming software of Schneider Automation; and an HMI logic [wherein the automation project comprises components that define functionality for the HMI] related to the objects created with an application generator 20 for different HMI products similar to other supervisory control and data acquisition (SCADA) software products” [Eller Column 7 line 60]: “The HMI 34 will work largely as a window into the process and will provide the operator [wherein the HMI provides interaction between the automation system with a human user or operator] with the various functions required in order to manage the process.” The motivation to combine with Eller is the same as the motivation to combine with Eller in claim 1. Regarding claim 23: The method of claim 1 is taught by Martinez, Eller, and Baier. PLCs are taught in aspects of claim 1. Eller teaches: wherein the automation project provides functionality for the PLC for control of the automation system, wherein the PLC monitors operation of the automation system and provides control of the system through various signals [Eller Column 3 near line 5]: "From the process design, a generator will generate the code for the PLC logic [wherein the automation project provides functionality for the PLC for control of the automation system, wherein the PLC monitors operation of the automation system and provides control of the system through various signals] , i.e., Concept projects, and the supervisory system, i.e., HMI applications.” Further support for aspects of a PLC is given in Eller, such as Figure 6 of Eller showing the PLC as part of the topological model where elements such as CPS, CPU, and other are being managed under the PLC. More support for PLC can be shown in Figure 7 of Eller indicating PLC (32) has elements such as communication, alarms and diagnostics, variables, etc. The motivation to combine with Eller is the same motivation to combine with Eller as claim 1 . 07-21-aia AIA Claim s 12, 15, 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Canedo et al (WO 2018140365 A1), referred to as Martinez in this document, and further in view of Eller et al (US 6643555), referred to as Eller in this document, and further in view of Murray et al (US 20190034785 A1), referred to as Murray in this document, and further in view of Baier et al (US 7930639), referred to as Baier in this document . Regarding Claim 12: Martinez teaches: A system for providing a knowledge representation in a cognitive engineering system (CES) comprising: a computer-based engineering tool for providing at least one of designing, programming simulation and testing of an automation system; [Martinez 0005]: “According to aspects of embodiments of the present invention, a method of performing cognitive engineering comprises, extracting human knowledge from at least one user tool [A system for providing a knowledge representation in a cognitive engineering system (CES) comprising: a computer-based engineering tool] , receiving system information from a cyber-physical system (CPS), organizing the human knowledge and the received system information into a digital twin graph (DTG), performing one or more machine learning techniques on the DTG to generate an engineering option [for providing at least one of designing, programming simulation and testing of an automation system] relating to the CPS, and providing the generated engineering option to a user in the at least one user tool.” Support for the tool being computer implemented is [Martinez 0015]: “The system may further comprise an extraction tool, operable by the computer processor, configured to record and save a time-sequence of user actions performed in the at least one user tool and store a historical record of a plurality of time-sequences of user actions in the database. The at least one user tool may include a computer aided technology (CAx)” Support for the engineering option being able to be at least one of designing, programming simulation and testing of an automation system is [Martinez 0049]: “This knowledge in the form of PiU data streams 405, are represented in the DTG 407, may be used by Deep Learning 409 and inference algorithms 41 1 to generate insights and requirements for a potential new product 413, the "spork", that combines the functionalities of both in a single utensil. Further PiU data 417 may be generated as the new product 413 is used and provided to update the DTG 407. Combined with the EaW, CENTAUR can then suggest the idea to the designers 415, and guide them step by step through the engineering process of the new product. The goal is to produce novel, useful, non-obvious products in a fraction of the time compared to the current product design practices.” a cognitive system in communication with the computer-based engineering tool comprising: a knowledge extraction module for identifying and storing information contained in an automation engineering project of the computer-based engineering tool and from data received from a physical automation system Aspects related to the physical automation system or other parts related to industrial engineering are taught in more detail by Eller later or by indications already given in claim 1. a machine learning module for analyzing knowledge extracted by the knowledge extraction module and identifying characteristics of the automation system; [Martinez 0015]: “A system for cognitive engineering according to aspects of embodiments of this disclosure comprise a database for extracting and storing user actions in at least one user tool [a cognitive system in communication with the computer-based engineering tool comprising: a knowledge extraction module for identifying and storing information contained in an automation engineering project of the computer-based engineering tool] , a cyber-physical system (CPS) comprising at least one physical component [and from data received from a physical automation system] , a computer processor in communication with the database [ further support of communication and knowledge extraction as the database is noted to be an element of the CPS and the graph is of the CPS ] and the at least one physical component configured to construct a digital twin graph representative of the CPS, and at least one machine learning technique [a machine learning module for analyzing knowledge extracted by the knowledge extraction module and identifying characteristics of the automation system] , executable by the computer processor and configured to generate at least one engineering option of the CPS.” Further support for the system using both data from a tool and physical parts of the system [Martinez 0004]: "Currently, there are attempts to integrate Al into CPS. For example, recent research showed that PLCs and edge devices, such as smart sensors, can be programmed using Al techniques to achieve new capabilities." The knowledge representation comprising a cognitive engineering graph (CEG), the CEG comprising a plurality of nodes representative of physical objects of the physical automation system in the automation engineering project or an automation program for controlling a corresponding physical object in the automation engineering project, and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes [Martinez 0033]: “Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) [The knowledge representation comprising a cognitive engineering graph (CEG), the CEG comprising a plurality of nodes representative of physical objects… and at least one edge connecting two of the nodes, the at least one edge representative of a relationship between the connected nodes] will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102.” physical objects of the physical automation system in the automation engineering project [Martinez 0015]: “A system for cognitive engineering according to aspects of embodiments of this disclosure comprise a database for extracting and storing user actions in at least one user tool, a cyber-physical system (CPS) comprising at least one physical component [physical objects of the physical automation system in the automation engineering project] , a computer processor in communication with the database and the at least one physical component configured to construct a digital twin graph representative of the CPS, and at least one machine learning technique, executable by the computer processor and configured to generate at least one engineering option of the CPS.” Further support is shown in the combination with Eller as shown later, as Eller teaches physical and non-physical elements (such as in Figure 6). a computer memory [Martinez 0055]: “The processors 720 may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art. More generally, a processor as used herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor may also comprise memory [a computer memory] storing machine-readable instructions executable for performing tasks. storing a plurality of CEGs from previously designed projects [Martinez Figure 6] PNG media_image1.png 452 656 media_image1.png Greyscale 609 DTG Historical Data shows that previous or historical data for projects exists [storing a plurality of CEGs from previously designed projects]. Further support of storing historical data related to industrial systems for use by machine learning or artificial intelligence is taught by Baier. in communication with the machine learning module for analyzing past knowledge [Martinez 0011]: “The DTG may change over time through at least one of the following: an addition of a node; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time creates a causal dependency that may be used by the one or more machine learning techniques [in communication with the machine learning module for analyzing past knowledge] to generate the engineering option.” a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project [Martinez 0055]: “A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface [a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project where the providing of the knowledge representation to the user is seen as assisting the user of the CES and the assistance seen as being able to be provided at any time(which would involve during design of the automation system) ] comprises one or more display images enabling user interaction with a processor or other device” Further support is given in combination with Eller, where Eller teaches indications of an HMI. Martinez does not explicitly teach: an automation program for controlling a corresponding physical object in the automation engineering project Martinez teaches elements of software that controls physical objects [Martinez 0048], but the specifics of an automation program for an automation engineering project are seen as better taught by Eller. wherein the CEG includes at least one node that represents a human machine interface (HMI) Martinez teaches the use of objects that can act as a human machine interface being a part of nodes, such as IoT devices in [Martinez 0039]. However the specific teaching of a HMI being a hardware equipment is better taught by Eller. and/or at least one node that represents a programmable logic controller (PLC) Martinez teaches PLCs and appears to indicate such elements are a part of the network of nodes, such as in [Martinez 0003]: “Cyber-Physical Systems (CPSs) components such as Programmable Logic Controllers (PLC) are programmed to do a specific task, but they are incapable of achieving self-awareness. Moreover, current CPSs lack the capability of artificial intelligence (Al).”. To better teach PLC as an element is better taught in Eller. an inductive programming module for automatically generating control programs for the automation system based on the stored information from the knowledge extraction module; Martinez does not explicitly teach using inductive programming for generating control programs, but Martinez does note improving software logic [Martinez 0048] and that PLCs could be programmed using AI techniques [Martinez 0004]. Eller teaches: an automation program for controlling a corresponding physical object in the automation engineering project [Eller Specification Technical Field Column 1 page 1]: “The present invention relates generally to an industrial automation system including software that is used to collect data, to monitor devices within an industrial environment and to trend characteristics of devices within an industrial environment for monitoring and/or controlling the industrial environment or control structure. More specifically, the present invention relates to automatically generating an application [an automation program for controlling a corresponding physical object in the automation engineering project] of a defined process for a control system.” wherein the CEG includes at least one node that represents a human machine interface (HMI) and/or at least one node that represents a programmable logic controller (PLC) PNG media_image2.png 1072 1365 media_image2.png Greyscale Figure 6 of Eller displays that models of the engineering system include elements such as PLC (top of Topological Model (30) [and/or at least one node that represents a programmable logic controller (PLC)] ) and HMI (middle of Topological Model (30) [wherein the CEG includes at least one node that represents a human machine interface (HMI)] ) a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project Eller teaches additional support for alternative or additional teachings for the feedback module, as Eller teaches HMIs. HMIs are seen as giving providing graphic representation of systems (thus providing representation of the system to a user where the user being given representation of the system can understand the system thus assisting the user). Eller also notes SCADA systems, which is seen as being relevant for feedback and control. [Eller Column 3 near line 5]: " The process objects will generally reside in the PLC and will include all attributes and variables associated with each object, including those currently used by the HMI e.g. graphic representation [a feedback module for providing knowledge representation from the cognitive system to a user, the knowledge representation assisting the user of the CES during design of the automation system in the automation engineering project] . " [Eller Column 5 line 10]: “Preferably, the application generator 20 generates controller logic for Concept, a control programming software of Schneider Automation; and an HMI logic related to the objects created with an application generator 20 for different HMI products similar to other supervisory control and data acquisition (SCADA) software products” The motivation to combine with Eller is the same motivation to combine with Eller in claim 1. Murray teaches: an inductive programming module for automatically generating control programs for the automation system based on the stored information from the knowledge extraction module [Murray 0026]: “As mentioned, Inductive programming (IP) [an inductive programming module for automatically generating control programs for the automation system where the use for control programs is shown by the use case and the combination with references teaching elements such as PLCs ] is an area of the broader field of automated computer programming, and includes research from both artificial intelligence and conventional computer programming disciplines. In a sense, inductive programming addresses the learning tasks needed for the development of declarative (in terms of logic or functionality) and often recursive programs from incomplete specifications. In some cases, the incomplete specification of the program may result from having limited input/output examples or may arise from the presence of constraints. Depending on the programming language used, there are several categories of inductive programming. These include inductive functional programming (which uses functional programming languages such as Lisp or Haskell), and inductive logic programming (which uses logic programming languages such as Prolog and/or other logical representations, such as description logics). Other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming.” [Murray 0027]: “In a general sense, Inductive Programming incorporates approaches concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs to an inductive programming system are a set of training inputs and corresponding outputs or an output evaluation function (describing the desired behavior of the intended program). Other possible inputs include [based on the stored information from the knowledge extraction module as this is showing inductive programming is known to take in inputs to base the generating off of. The use of the knowledge extraction module is done by the combination of references as the knowledge extraction module is acting as the element to provide information to the inductive programming module ] : traces or action sequences which describe the process of calculating specific outputs; constraints for the program concerning its time efficiency or its complexity; relevant background knowledge such as standard data types; predefined functions to be used; program schemes or templates describing the data flow of the intended program; or, heuristics for guiding the search for a solution or other biases.” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Martinez and Murray. Martinez and Murray are in the same field of endeavor of machine learning. One of ordinary skill in the art, prior to the effective filing date would have been motivated to combine Martinez with Murray to utilize inductive programming, as inductive programming follows the premise established in Martinez ([Martinez 0004]: “Currently, there are attempts to integrate Al into CPS. For example, recent research showed that PLCs and edge devices, such as smart sensors, can be programmed using Al techniques to achieve new capabilities.”) and enables the creation of programs using incomplete specifications ([Murray 0026]: “As mentioned, Inductive programming (IP) is an area of the broader field of automated computer programming, and includes research from both artificial intelligence and conventional computer programming disciplines. In a sense, inductive programming addresses the learning tasks needed for the development of declarative (in terms of logic or functionality) and often recursive programs from incomplete specifications. In some cases, the incomplete specification of the program may result from having limited input/output examples or may arise from the presence of constraints.”). The combination is also sensible with the teachings of Eller, as Eller too teaches that tools or systems can create logic for the PLC and such ([Eller Column 3 near line 5]: “From the process design, a generator will generate the code for the PLC logic, i.e., Concept projects, and the supervisory system, i.e., HMI applications.”). Baier alternatively teaches: storing a plurality of CEGs from previously designed projects [Baier Column 12 Line 10]: “Typically, the system 1000 can be viewed as a Distributed Historian [storing a plurality of CEGs from previously designed projects] that spans machines, plants, and enterprises. At level 1030, the historian collects data at the rack level and is coupled to Common Plant Data Structure described above. Such can include collecting process & discrete data, alarms & events in a single archive if desired.” Historians are noted as being elements that stores data for use of correlations ([Baier Column 2 Line 27]: “The subject innovation provides for a historian(s) with a correlation component(s) that discovers relations and correlates among disparate pieces of data, to infer possible relationships between historian data/events and the industrial process (e.g., predict an outcome thereof). The correlation component can employ a heuristic model to capture process data/event data, and further include an implicit correlation component and an explicit correlation component. The explicit correlation component can employ predetermined models that are set by a user/external data sources, and the implicit correlation component can deduce relations among causes of triggering events (e.g., dynamically and/or in real-time during operation). For example, instead of merely storing values, messages that cause transition of values can be stored and compared via the implicit correlation component to derive correlations among various states that share the same messages. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process.”) The relation to AI is shown in ([Baier Column 6 Line 56]: “In a related aspect, the inference component 610 can further employ an artificial intelligence (AI) component to facilitate correlating outcome of an industrial process with historian data and/or events.”) The motivation to combine with Baier is the same as the motivation to combine with Baier in claim 1. Regarding Claim 15: The system of claim 12 is taught by Martinez, Eller, Murray, and Baier. Martinez teaches: wherein the feedback module is configured to provide the user with a design recommendation for the automation engineering project based on an output from the machine learning module [Martinez 0049]: “FIG. 4 is an illustration of a potential benefit of PiU. According to one non- limiting example, CENTAUR can parse through millions of pictures and videos of people having a barbecue. After labeling, millions of forks 401 and spatulas 403 are identified as utensils commonly used in barbecues. This knowledge in the form of PiU data streams 405, are represented in the DTG 407, may be used by Deep Learning 409 and inference algorithms 41 1 to generate insights and requirements for a potential new product 413, the "spork", that combines the functionalities of both in a single utensil. Further PiU data 417 may be generated as the new product 413 is used and provided to update the DTG 407. Combined with the EaW, CENTAUR can then suggest the idea to the designers [wherein the feedback module is configured to provide the user with a design recommendation for the automation engineering project based on an output from the machine learning module] 415, and guide them step by step through the engineering process of the new product. The goal is to produce novel, useful, non-obvious products in a fraction of the time compared to the current product design practices.” Regarding Claim 16: The system of claim 12 is taught by Martinez, Eller, Murray, and Baier. Martinez teaches: a communication channel between the physical automation system and the knowledge extraction module for extracting operations data from the automation system for analysis by the cognitive system [Martinez 0005]: “According to aspects of embodiments of the present invention, a method of performing cognitive engineering comprises, extracting human knowledge from at least one user tool, receiving system information from a cyber-physical system (CPS) [a communication channel between the physical automation system and the knowledge extraction module for extracting operations data from the automation system for analysis by the cognitive system] , organizing the human knowledge and the received system information into a digital twin graph (DTG), performing one or more machine learning techniques on the DTG to generate an engineering option relating to the CPS , and providing the generated engineering option to a user in the at least one user tool.” Regarding Claim 17: The system of claim 12 is taught by Martinez, Eller, Murray, and Baier. Martinez teaches: further comprising an automated reasoning module in communication with the knowledge representation and the machine learning module the automated reasoning module configured to automatically add a component to the automation engineering project based on the knowledge representation and the machine learning module [Martinez 0040]: “The DTG 101 is dynamic in the sense that the graph is continuously evolving with the creation and elimination of nodes [the automated reasoning module configured to automatically add a component to the automation engineering project based on the knowledge representation and the machine learning module] 203 and edges 201. This is because the DTG 101 is continuously updated by data, queries, simulation, models [ models and the other data sources listed are seen as things able to cause the addition a component ] , new providers, new consumers, and dynamic relationships between them. Even though the DTG 101 may consist of a large graph with billions of nodes 203 and edges 201 , existing databases (e.g., GraphX, Linked Data) and algorithms (e.g. Pregel, MapReduce) running in cloud platforms may help to efficiently search and update the DTG 101. The DTG 101 representation is also suitable for a smooth integration with novel mathematical engines based on graph-theoretic and categorical approaches. The constant spatio-temporal evolution of the DTG 101 is captured in terms of a time-series of snapshots. The current snapshot of the DTG 101 reports the status of the operational environment (OE) and the OE's components such as CPS. Snapshots in the past provide a historical perspective that can be used to identify known patterns with supervised learning [further comprising an automated reasoning module in communication with the knowledge representation and the machine learning module] , and unknown patterns with unsupervised learning. After these learned models are created, the DTG 101 can also be used to predict outcomes.” Further support that steps can be automatic [Martinez 0065]: “The functions and process steps herein may be performed automatically or wholly or partially in response to user command. An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity” . 07-21-aia AIA Claim s 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Canedo et al (WO 2018140365 A1), referred to as Martinez in this document, and further in view of Eller et al (US 6643555), referred to as Eller in this document, and further in view of Baier et al (US 7930639), referred to as Baier in this document, and further in view of Glafkides (US 20200184334 A1), referred to as Glafkides in this document . Regarding Claim 5: The method of claim 4 is taught by Martinez, Eller, and Baier. Martinez teaches: to remove the element that was automatically added to the CEG [Martinez 0011]: “The DTG may change over time through at least one of the following: an addition of a node [to remove the element that was automatically added to the CEG] ; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time creates a causal dependency that may be used by the one or more machine learning techniques to generate the engineering option.” Martinez does not explicitly teach: performing an undo action by the CES at a request of the same or a different user Glafkides teaches: performing an undo action by the CES at a request of the same or a different user [Glafkides 0050]: “If the modified NNT 122 (e.g., the modified information in the data file 127) breaks a source rule (e.g., the modified NNT 122 does not verify), the source rule module 110 may direct display of a notice via the GUI. In some embodiments, the notice may include information describing the source rules that the modified NNT 122 breaks. In these and other embodiments, the notice may include information describing portions of the modified NNT 122 that breaks the source rules. In some embodiments, the notice may also include an option (e.g., a request) to confirm proceeding using the modified NNT 122 despite the modified NNT 122 not being verified. In these and other embodiments, the notice may include an option [at a request of the same or a different user] to revert back to the NNT prior to the modifications (e.g., undo the modifications [performing an undo action by the CES] ).” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Martinez and Glafkides. Martinez and Glafkides are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Martinez and Glafkides to be able to undo an action as undoing an action allows reversing a change that causes problems in a system ([Glafkides 0050]: “If the modified NNT 122 (e.g., the modified information in the data file 127) breaks a source rule (e.g., the modified NNT 122 does not verify), the source rule module 110 may direct display of a notice via the GUI.) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nickel et al (“A Review of Relational Machine Learning for Knowledge Graphs”) is relevant art that discusses storing data, knowledge graphs, giving recommendations, and other insights on the usage of a graph structure. Zhang et al (“Deep Learning Based Recommender System: A Survey and New Perspectives”) is relevant art that discusses the use of machine learning on giving recommendations using historical data such as user or item interactions. Also notes the usage of knowledge graphs for recommendation systems. Panchal et al (“Special Issue: Machine Learning for Engineering Design”) is relevant art that discusses how machine learning is or can be used to assist in engineering design, which is relevant to the current application using machine learning to assist in engineering design for automation. The application also provides motivation to aid engineering design with machine learning. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER D DEVORE whose telephone number is (703)756-1234. The examiner can normally be reached Monday-Friday 7:30 am - 5 pm EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /C.D.D./Examiner, Art Unit 2129 /MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129 Application/Control Number: 17/782,678 Page 2 Art Unit: 2129 Application/Control Number: 17/782,678 Page 3 Art Unit: 2129 Application/Control Number: 17/782,678 Page 4 Art Unit: 2129 Application/Control Number: 17/782,678 Page 5 Art Unit: 2129 Application/Control Number: 17/782,678 Page 6 Art Unit: 2129 Application/Control Number: 17/782,678 Page 7 Art Unit: 2129 Application/Control Number: 17/782,678 Page 8 Art Unit: 2129 Application/Control Number: 17/782,678 Page 9 Art Unit: 2129 Application/Control Number: 17/782,678 Page 10 Art Unit: 2129 Application/Control Number: 17/782,678 Page 11 Art Unit: 2129 Application/Control Number: 17/782,678 Page 12 Art Unit: 2129 Application/Control Number: 17/782,678 Page 13 Art Unit: 2129 Application/Control Number: 17/782,678 Page 14 Art Unit: 2129 Application/Control Number: 17/782,678 Page 15 Art Unit: 2129 Application/Control Number: 17/782,678 Page 16 Art Unit: 2129 Application/Control Number: 17/782,678 Page 17 Art Unit: 2129 Application/Control Number: 17/782,678 Page 18 Art Unit: 2129 Application/Control Number: 17/782,678 Page 19 Art Unit: 2129 Application/Control Number: 17/782,678 Page 20 Art Unit: 2129 Application/Control Number: 17/782,678 Page 21 Art Unit: 2129 Application/Control Number: 17/782,678 Page 22 Art Unit: 2129 Application/Control Number: 17/782,678 Page 23 Art Unit: 2129 Application/Control Number: 17/782,678 Page 24 Art Unit: 2129 Application/Control Number: 17/782,678 Page 25 Art Unit: 2129 Application/Control Number: 17/782,678 Page 26 Art Unit: 2129 Application/Control Number: 17/782,678 Page 27 Art Unit: 2129 Application/Control Number: 17/782,678 Page 28 Art Unit: 2129 Application/Control Number: 17/782,678 Page 29 Art Unit: 2129 Application/Control Number: 17/782,678 Page 30 Art Unit: 2129 Application/Control Number: 17/782,678 Page 31 Art Unit: 2129 Application/Control Number: 17/782,678 Page 32 Art Unit: 2129 Application/Control Number: 17/782,678 Page 33 Art Unit: 2129 Application/Control Number: 17/782,678 Page 34 Art Unit: 2129 Application/Control Number: 17/782,678 Page 35 Art Unit: 2129 Application/Control Number: 17/782,678 Page 36 Art Unit: 2129 Application/Control Number: 17/782,678 Page 37 Art Unit: 2129 Application/Control Number: 17/782,678 Page 38 Art Unit: 2129 Application/Control Number: 17/782,678 Page 39 Art Unit: 2129 Application/Control Number: 17/782,678 Page 40 Art Unit: 2129 Application/Control Number: 17/782,678 Page 41 Art Unit: 2129 Application/Control Number: 17/782,678 Page 42 Art Unit: 2129 Application/Control Number: 17/782,678 Page 43 Art Unit: 2129 Application/Control Number: 17/782,678 Page 44 Art Unit: 2129 Application/Control Number: 17/782,678 Page 45 Art Unit: 2129 Application/Control Number: 17/782,678 Page 46 Art Unit: 2129 Application/Control Number: 17/782,678 Page 47 Art Unit: 2129 Application/Control Number: 17/782,678 Page 48 Art Unit: 2129 Application/Control Number: 17/782,678 Page 49 Art Unit: 2129 Application/Control Number: 17/782,678 Page 50 Art Unit: 2129 Application/Control Number: 17/782,678 Page 51 Art Unit: 2129 Application/Control Number: 17/782,678 Page 52 Art Unit: 2129 Application/Control Number: 17/782,678 Page 53 Art Unit: 2129 Application/Control Number: 17/782,678 Page 54 Art Unit: 2129 Application/Control Number: 17/782,678 Page 55 Art Unit: 2129 Application/Control Number: 17/782,678 Page 56 Art Unit: 2129 Application/Control Number: 17/782,678 Page 57 Art Unit: 2129 Application/Control Number: 17/782,678 Page 58 Art Unit: 2129 Application/Control Number: 17/782,678 Page 59 Art Unit: 2129 Application/Control Number: 17/782,678 Page 60 Art Unit: 2129 Application/Control Number: 17/782,678 Page 61 Art Unit: 2129 Application/Control Number: 17/782,678 Page 62 Art Unit: 2129
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