DETAILED ACTION
Claims 15-30 are presented for examination.
This Office Action is in response to submission of documents on March 9, 2026.
Objection to the Specification is withdrawn.
Interpretation of claim 30 under 35 U.S.C. 112(f) is withdrawn.
Rejection of claim 30 under 35 U.S.C. 112(a) for failing to comply with the written description requirement is withdrawn.
Rejection of claim 30 under 35 U.S.C. 112(b) for being indefinite is withdrawn.
Rejection of claims 15-30 under 35 U.S.C. 101 for being directed to unpatentable subject matter is maintained.
Rejection of claims 15-20, 26-28, and 30 under 35 U.S.C. 102(a)(2) as being anticipated by Thomsen is withdrawn.
Rejection of claims 21-23 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Benesh is withdrawn.
Rejection of claims 24-25 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Deutsch is withdrawn.
Rejection of claims 29 is rejected under 35 U.S.C. 103 as being obvious over Thomsen in view Cella is withdrawn.
Rejection of claims 15-20, 26-28, and 30 under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey.
Rejection of claims 21-23 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Benesh.
Rejection of claims 24-25 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Deutsch.
Rejection of claims 29 is rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Cella.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding Objection to the Specification, the Response includes amendments that appear to address the Objection. Accordingly, the Objection to the Specification is withdrawn.
Regarding interpretation of claim 30 as including functional language, the amendments to the claim appear to obviate the interpretation under 35 U.S.C. 112(f). Accordingly, the interpretation is withdrawn. Further, the amendments appear to address the rejection under 35 U.S.C. 112(a). Accordingly, the rejection of claim 30 under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) are withdrawn.
Regarding rejection of claims 15-30 as being directed to unpatentable subject matter under 35 U.S.C. 101, Examiner is not persuaded by the amendments and arguments for the following reasons:
Applicant asserts that “supplying a holistic digital twin of the industrial facility to different tools so as to permit crosslinks between different tools based on unique asset identifiers to be automatically established” integrates the judicial exception(s) into a practical application. Response at pg. 10. However, as indicated by the Applicant, the supplying of the digital twin permits the establishment of crosslinks, but the claim, as amended, does not require the crosslinks to be established. Thus, the claim does not explicitly claim that the crosslinks are established nor what is performed with the digital twin once supplied to the different tools.
Further, the claim, as amended, does not improve the functioning of a computer, as asserted by the Applicant. The claim does not recite limitations that improve, for example, improvements in memory usage or reduced computing time so as to recite a computer improvement as interpreted by courts and the MPEP. See, e.g., MPEP 2106.04(d)(1); MPEP 2106.05(a).
Applicant asserts that the Specification discloses improvements in the “technological field of industrial facilities (automation systems), and is therefore in fact limited to a useful practical application.” Response at pg. 12. However, in citing to the Specification, Applicant indicates “The holistic digital twin provides predictive analytical functions that allow improvement of a product design, a process design as well as maintenance activities…The holistic digital twin makes it possible to duplicate and predict in a virtual world properties and performance features of a physical product, product line, manufacturing process of a complete industrial facility before a single item is physically acquired or produced.” Spec at [0007]. While the disclosed improvements may be accomplished by using the claimed invention, the claims themselves do not recite limitations to “improvement of a product design, a process design as well as maintenance activities” nor “duplicat[ing] and predict[ing] in a virtual world properties and performance features of” products and processes. Thus, the claims do not reflect the alleged improvement because the claims do not recite steps of, for example, performing duplication, predictions, maintenance activities, etc. Further, the additional cited portions of the Specification cite similar improvements and, for the same reasons, are unpersuasive.
Of additional note, any improvements and/or integration into a practical application cannot come from a judicial exception but instead must originate from an additional element that is not a judicial exception. “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.” MPEP 2106.05(a). Accordingly to the rejection, the additional elements are mere data gathering, data transmission, and an idea of a solution. Regarding the “automatically establish crosslinks between the different tools based on the unique asset identifiers,” the claim does not recite, with specificity, how the crosslinks are established nor how the unique asset identifiers are utilized to establish the crosslinks. Thus, the limitation is an idea of a solution.
Accordingly, because Examiner has not found the arguments and amendments persuasive, the rejection of claims 15-20 under 35 U.S.C. 101 is maintained.
Regarding rejection of claims 15-20, 26-28, and 30 under 35 U.S.C. 102(a)(2) as being anticipated by Thomsen, Examiner agrees that the reference does not explicitly disclose “unique identifiers.” Although asset identifiers are each unique within the context of the reference, Thomsen does not disclose this as a feature and/or limitation of the disclosed system. Accordingly, rejection of claims 15-20, 26-28, and 30 under 35 U.S.C. 102(a)(2) are withdrawn. However, in light of the amendments, a new rejection under 35 U.S.C. 103 is asserted for claims 15-20, 26-28, and 30 as being obvious over Thomsen in view of Hershey. The rejection of the other claims under 35 U.S.C. 103 are newly asserted using the same references and further in view of Hershey.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical calculations and mental processes. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that the steps of data gathering, ideas of solutions, and generally linking the judicial exceptions to a particular field or technology are not significantly more than the judicial exception.
Claim 15
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 15
Mapping Under Step 2A Prong 1
A computer-implemented method for providing a holistic digital twin of an industrial facility comprising a plurality of assets, the method comprising:
(a) converting asset related data collected from different tools utilized to at least one of plan and operate said industrial facility in a tool specific data format and asset related data provided by data sources of the industrial facility in a data source specific data format into a common graphical representation, each asset of the plurality of assets being associated with a unique asset identifier provided across said different tools and data sources;
(b) matching common graphical representations of the converted asset related data to provide a mapping between assets of said industrial facility;
(c) merging mapped assets of said industrial facility into a unified graph to provide the holistic digital twin of said industrial facility, nodes of the unified graph including unique asset identifiers; and
(d) supplying the holistic digital twin of the industrial facility to the different tools to automatically establish crosslinks between the different tools based on the unique asset identifiers;
wherein the common graphical representation of converted asset related data comprises for each asset a node connected via edges to other nodes representing other assets of said industrial facility having a physical or logical relation with a respective asset.
Abstract Idea: Mathematical Calculations
A digital twin is a mathematical construct that includes functions that simulate the activities that are performed in a physical industrial facility. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Converting data from one format to a common graphical representation is a mental process that can be performed by a human. The human can identify the type of tool or data source that generated the data and select a graphical representation to use as an image in a graph for the tool or source. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Matching graphical representation can be performed by a human, either with pencil and paper or with a computer as an aid (which courts have found can still be a mental process. See MPEP 2106.04(a)(2), Subsection III(C)). For example, a human can use a graphing program to select representations and draw a connection between related assets.
Abstract Idea: Mental Process
Merging connected graphical representations into a single map can be performed by a human, either with pencil and paper or with a computer as an aid (which courts have found can still be a mental process. See MPEP 2106.04(a)(2), Subsection III(C)). For example, a human can use a graphing program to select connected representations and add them together to form a merged map.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 15
Mapping Under Step 2A Prong 2
A computer-implemented method for providing a holistic digital twin of an industrial facility comprising a plurality of assets, the method comprising:
(a) converting asset related data collected from different tools utilized to at least one of plan and operate said industrial facility in a tool specific data format and asset related data provided by data sources of the industrial facility in a data source specific data format into a common graphical representation, each asset of the plurality of assets being associated with a unique asset identifier provided across said different tools and data sources;
(b) matching common graphical representations of the converted asset related data to provide a mapping between assets of said industrial facility;
(c) merging mapped assets of said industrial facility into a unified graph to provide the holistic digital twin of said industrial facility, nodes of the unified graph including unique asset identifiers;
(d) supplying the holistic digital twin of the industrial facility to the different tools to
automatically establish crosslinks between the different tools based on the unique asset identifiers;
wherein the common graphical representation of converted asset related data comprises for each asset a node connected via edges to other nodes representing other assets of said industrial facility having a physical or logical relation with the respective asset.
Collecting data from tools and other data sources is an extra-solution activity of data gathering, which courts have found does not integrate the recited judicial exception(s) into a practical application. See MPEP 2106.05(g)(3). Further, generally linking the use of the judicial exception to a field of use does not integrate the judicial exception into a practical application. MPEP 2106.05(h).
Alternatively, providing the holistic digital twin is an idea of a solution without claiming specifically how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
“Supplying” is the extra-solution activity of data transmission, which courts have found does not integrate a judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
The limitation is an idea of a solution that is not recited with specificity (e.g., details as to how the solution is accomplished) such that it integrates the judicial exception into a practical application and/or improves a technology. See MPEP 2106.05(f)(1).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Courts have found that mere data gathering to be insignificant extra-solution activity. See, e.g., Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Additionally, generally linking a judicial exception to a particular field or technology is insignificantly more than the recited judicial exception. See CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). Further, ideas of a solution has been determined by courts to be insignificantly more than the recited judicial exception. 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).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 16
Claim 16 recites wherein the assets of said industrial facility comprise hardware components and software components installed in said industrial facility. The limitation merely further generally links the judicial exception to a field of use, which courts have found to be insignificantly more than the judicial exception and further do not integrate the exception into a practical application. Accordingly, claim 16 is rejected for being directed to unpatentable subject matter.
Claim 17
Claim 17 recites wherein each asset of said industrial facility comprises an associated unique asset identifier. The limitation further specifies data that is associated with assets, which is used as part of the mathematical concepts and/or mental process without adding additional elements that integrate the exception into a practical application. Accordingly, claim 17 is rejected for being directed to unpatentable subject matter.
Claim 18
Claim 18 recites wherein each asset of said industrial facility comprises an associated unique asset identifier. The limitation further specifies data that is associated with assets, which is used as part of the mathematical concepts and/or mental process without adding additional elements that integrate the exception into a practical application. Accordingly, claim 18 is rejected for being directed to unpatentable subject matter.
Claim 19
Claim 19 recites wherein the common graphical representations generated by the conversions of the asset related data are stored as unified data in a central storage. Storing data in a database is related to mere data gathering, which courts have found is insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Accordingly, claim 19 is rejected for being directed to unpatentable subject matter.
Claim 20
Claim 20 recites wherein the matching of the common graphical representations is performed by a graph matching algorithm. Using a graph matching algorithm is a mathematical concepts that requires comparison between values of the graphical representations and selecting one or more of the representations as matches. Thus, the limitation is directed to a judicial exception of mathematical concepts. See MPEP 2106.04(a)(2), Subsection I. Accordingly, claim 20 is rejected for being directed to unpatentable subject matter.
Claim 21
Claim 21 recites wherein at least one of ambiguities and mismatches occurring during the matching of the common graphical representations are resolved in response to a user input. Getting input from a user is data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exceptions into a practical application. Further, resolving ambiguities and mismatches is a step that can be performed by a human using observation, evaluation, opinion, and judgment. Thus, the limitation recites a judicial exception of a mental process. Accordingly, claim 21 is rejected for being directed to unpatentable subject matter.
Claim 22
Claim 22 recites wherein at least one of ambiguities and mismatches occurring during matching of the common graphical representations are resolved automatically based on received asset related data concerning assets affected by observed at least one of ambiguities and mismatches triggered by the graph matching algorithm in response to detected at least one of ambiguities and mismatches. According, claim 22 is rejected for being directed to unpatentable subject matter.
Claim 23
Claim 23 recites wherein the graph matching algorithm is machine learned. Using a machine learning model is a mathematical concepts because the machine learning model is comprised of one or more mathematical function, takes one or more parameters as input, and provides a result after performing the functions. Accordingly, claim 23 is directed to unpatentable subject matter.
Claim 24
Claim 24 recites wherein the different tools providing the asset related data comprise tools of different lifecycle stages of said industrial facility. The limitation merely generally links the judicial exception to a particular field of use. See MPEP 2106.05(h). See also, e.g., Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016). Accordingly, claim 24 is directed to unpatentable subject matter.
Claim 25
Claim 25 recites wherein the tools of the different lifecycle stages of said industrial facility comprise at least one of engineering tools, operation management tools and service and maintenance tools. The limitation merely further specifies the field of use for the judicial exception. For example, the type of tools that are included in the industrial facility is not an additional element but is instead a further specification of already claimed tools, which are either part of steps that are judicial exceptions or insignificant additional elements that do not integrate the judicial exception into a practical application. Accordingly, claim 25 is directed to unpatentable subject matter.
Claim 26
Claim 26 recites wherein the generated holistic digital twin is fed back to the tools utilized to at least one of plan and operate said industrial facility to upgrade the respective tools. The limitations recite an idea of a solution without specific details as to how the solution is accomplished. For example, “feeding” the twin back to the tools is not recited with specificity as to how the tool is provided the twin, and further, upgrading the tool(s) is not claimed with details as to how the upgrade is performed. Accordingly, claim 26 is directed to unpatentable subject matter.
Claim 27
Claim 27 recites wherein the generated holistic digital twin of the industrial facility is processed to at least one of simulate and predict an operational behavior of said industrial facility. Simulating operational behavior is a mathematical concept because a simulation includes one or more mathematical functions that are representative of the physical assets of the industrial facility. Further, predicting operational behavior is either a mathematical concepts (if the prediction is part of the simulation) or a mental process that requires observation, evaluation, opinion, and judgment. Accordingly, claim 27 is directed to unpatentable subject matter.
Claim 28
Claim 28 recites wherein data sources of the industrial facility providing asset related data in a data source specific data format comprise at least one of sensor assets and memory assets of said industrial facility. The limitations merely specifies a source of data, which courts have found to be insignificant extra-solution activities that do not incorporate the recited judicial exception into a practical application. See, e.g., Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937. Accordingly, claim 28 is directed to unpatentable subject matter.
Claim 29
Claim 29 recites wherein asset related data provided in a tool specific data format of a tool comprise at least one of image data representing the assets, acoustic data of sounds generated by the assets, text data describing the assets, graphical data, topological data of an asset topology and location data of asset locations. The limitation merely specifies types of data that can be provided from a tool or data source. As previously asserted, gathering of such data is an insignificant extra-solution activity that does not integrate the judicial exceptions into a practical application. Accordingly, claim 29 is directed to unpatentable subject matter.
Claim 30
Claim 30 recites a system for generating a holistic digital twin of an industrial facility which comprises a plurality of assets, said system comprising: a processor; and a central storage; wherein the processor is configured to perform steps substantially the same as recited in claim 15. The claim recites generic computer components that perform the recited judicial exceptions, which courts have found to be an insignificant additional element that does not integrate the judicial exception into a practical application. Accordingly, for at least the same reasons as claim 15, claim 30 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 15-20, 26-28, and 30 are rejected under 35 U.S.C. 103 as being obvious over Thomsen (U.S. Pat. Pub. No. 2020/0012265) in view of Hershey, et al., (U.S. Pat. Pub. No. 2017/0286572, hereinafter “Hershey”).
Claim 15
Thomsen discloses:
A computer-implemented method for providing a holistic digital twin of an industrial facility comprising a plurality of assets, the method comprising:
Also, one or more embodiments provide a method, comprising defining, on a system comprising a processor based on configuration input data, a digital twin that defines an industrial asset in terms of hierarchical elements… Thomsen at [0004].
(a) converting asset related data collected from different tools utilized to at least one of plan and operate said industrial facility in a tool specific data format and
Industrial assets and their associated industrial assets can generate large amounts of information during operation. FIG. 2 is a conceptual diagram illustrating the flow of industrial data across various information levels in a typical industrial environment. Thomsen at [0066].
Industrial controllers 202 perform supervisory monitoring and control of the industrial assets 206 via industrial devices 204. In this regard, industrial devices 204 serve as inputs and outputs for industrial controllers 202, which control their output industrial devices in accordance with user-defined control routines (e.g., ladder logic programs, sequential function chart programs, etc.) and the current values and statuses of the input industrial devices… Thomsen at [0067].
I/O control component 306 can be configured to control the electrical output signals of the industrial device's digital and analog electrical outputs in accordance with the control program outputs, and to convert electrical signals on the industrial device's analog and digital inputs to data values that can be processed by the program execution component 304. Thomsen at [0075].
The “industrial data” is analogous to the “asserts related data,” which is collected from “associated industrial assets.”
The “industrial assets” include “industrial controllers,” which generate data “during operation.”
asset related data provided by data sources of the industrial facility in a data source specific data format
Some industrial environments may also include other systems or devices relating to specific aspects of the controlled industrial systems. These may include, for example, a data historian 110 that aggregates and stores production information collected from the industrial controllers 118 or other data sources, or a device documentation store 104 containing electronic documentation for the various industrial devices making up the controlled industrial systems. Thomsen at [0065].
The “data historian” is a data source.
into a common graphical representation,
Example production model 1102 has a single plant node 1104, below which are multiple line nodes 1106 (Line 1, Line 2, and Line 3), which are child nodes relative to plant node 1104. Line nodes 1106 represent various production lines within the plant represented by plant node 1104. Each line node 1106 has a number of child machine nodes 1108 representing machines deployed on the line represented by the associated line node 1106 (e.g., Cartoner, Case Packer, Flow Wrap, Packaging System). Each machine node 1108 is associated with a number of monitored values 1110… Thomsen at [0114].
See also FIG. 11, illustrating the “graphical representation.”
(b) matching common graphical representations of the converted asset related data to provide a mapping between assets of said industrial facility; and
In the case of the asset model 422, model configuration application 2502 allows a user to create nodes representing an industrial facility, production lines or areas within the industrial facility, industrial assets (e.g., industrial machine, industrial robots, etc.) within each production line, units of equipment associated with a given industrial asset (e.g., a loader, a pusher, a machining station, etc.), and/or industrial devices (e.g., controllers, drives, etc.) associated with the industrial asset being modeled. The user can then assign selected BIDTs 322 (representing measured control values, events, states, odometer counts, etc.) to respective nodes of the asset model 422, as described above in connection with FIG. 10. Thomsen at [0167].
The “nodes” are graphical representations of asset data (see FIG. 11), and are connected to provide a “mapping.”
(c) merging mapped assets of said industrial facility into a unified graph to provide the holistic digital twin of said industrial facility,
FIG. 22 illustrates an example methodology 2200 for aggregating asset models and using the aggregated model to generate graphical presentations of industrial data. Initially, at 2202, multiple asset models representing respective industrial assets or groups of assets are received from one or more gateway devices. As in previous examples, the asset models define groupings of BIDT data tags within hierarchical organizations of plant elements. At 2204, the asset models are integrated (e.g., at an application server system) to yield a plant model, which defines a hierarchical plant or enterprise structure comprising multiple industrial assets. Thomsen at [0147]-[0148].
“Aggregating asset models” is analogous to “merging mapped assets.”
(d) supplying the holistic digital twin of the industrial facility to the different tools to automatically establish crosslinks between the different tools
Control component 3004 can be linked to predictive analysis component 512 such that supervisory program 3006 is notified when a performance issue is predicted, and supervisory program 3006 can be configured to, in response to a predicted performance issue, generate control data 3018 designed to alter control of the automation system 3008 in a manner that mitigates the predicted performance issue. Control data 3018 can include, for example, instructions to modify one or more control setpoints defined in the local industrial controller 3014, instructions to the local industrial controller 3014 to begin executing an alternate control routine, instructions to alter an operating mode of the automation system 3008, or other such instructions. In the illustrated example architecture, device interface component 2814 sends control data 3018 to the industrial controller 3014 via gateway device 3010. Thomsen at [0190].
Control component 3004 and predictive analysis component 512 are “different components” and are “crosslinked” (i.e., “linked” to each other). Each of the components has access to (i.e., are “supplied”) the digital twin 2306 (See FIG. 30).
wherein the common graphical representation of converted asset related data comprises for each asset a node connected via edges to other nodes representing other assets of said industrial facility having a physical or logical relation with the respective asset.
As can be seen in the example asset structure models of FIGS. 11 and 12, the BIDTs are properties of their associated parent nodes. For example, the monitored values 1110 of the Cartoner machine—which are obtained from respective BIDT data tags on one or more industrial devices 302—are properties of the Cartoner product node 1108. During model development, the user can define the various plant nodes, line nodes, product nodes, equipment nodes, or other types of nodes that make up an industrial enterprise as a whole, or a particular set of industrial applications within the industrial enterprise, and define the hierarchical relationships between these nodes. The user can then assign selected BIDTs to their appropriate nodes to yield the asset model, which can be downloaded and stored on the gateway device 402. Thomsen at [0117].
See also FIG. 11, illustrating the “edges” connecting the various “nodes,” each representing an “asset” and/or data from a sensor deployed on an “asset.”
Thomsen does not appear to explicitly disclose:
each asset of the plurality of assets being associated with a unique asset identifier provided across said different tools and data sources;
nodes of the unified graph including unique asset identifiers;
crosslinks between the different tools based on the unique asset identifiers;
Hershey, which is analogous art, discloses:
each asset of the plurality of assets being associated with a unique asset identifier provided across said different tools and data sources;
Referring to FIG. 25, a table is shown that represents the digital twin database 2500 that may be stored at the digital twin platform 2400 according to some embodiments. The table may include, for example, entries identifying sensor measurement associated with a digital twin of a twinned physical system. The table may also define fields 2502, 2504, 2506, 2508 for each of the entries. The fields 2502, 2504, 2506, 2508 may, according to some embodiments, specify: a digital twin identifier 2502, engine data 2504, engine operational status 2506, and vibration data 2508. The digital twin database 2500 may be created and updated, for example, when a digital twin is created, sensors report values, operating conditions change, etc. The digital twin identifier 2502 may be, for example, a unique alphanumeric code identifying a digital twin of a twinned physical system. The engine data 2504 might identify a twinned physical engine identifier, a type of engine, an engine model, etc. Hershey at [0147]-[0148].
nodes of the unified graph including unique asset identifiers;
Interacting digital twins may be used to perform cooperative experiments on their twinned physical systems in order to tune the Interacting digital twin models. This mode is termed the “de minimis” mode as the interacting digital twins are permitted to experiment on their twinned physical systems by actively varying the controls in a very limited manner in order to perform the model tuning protocols. 2230 (as opposed to an analysis of produced data associated with full authority control 2240). One example of such an approach is illustrated in FIG. 23 wherein a set of eight Interacting Digital Twins (“IDTs”) 2300 are each monitoring a twinned physical system such as indicated by IDT#1 2310 monitoring its twinned physical system 2320. Hershey at [0129].
Twinned physical system 2320 has a unique identifier, which is utilized by interacting digital twin 2310 in monitoring the component. The components are unified in a communication graph, as illustrated in FIG. 23, each of which includes the information illustrated in the database illustrated in FIG. 25.
crosslinks between the different tools based on the unique asset identifiers;
a set of eight Interacting Digital Twins (“IDTs”) 2300 are each monitoring a twinned physical system such as indicated by IDT#1 2310 monitoring its twinned physical system 2320. Hershey at [0129].
Hershey is analogous art to the claimed invention because both are directed to generating digital twins of an industrial facility. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the unique identifiers of Hershey with the digital twin process of Thomsen to enforce a unique identifier on each asset in the digital twin system. Motivation to combine includes ensuring that a particular entity, when only presented as an identifier, is a reference to only one asset. Thus, instead of providing larger data structures of information to components, identifiers can be utilized instead, thus reducing computing resources that would otherwise be required to provide a reference to a component.
Claim 16
Thomsen discloses:
wherein the assets of said industrial facility comprise hardware components and software components installed in said industrial facility.
Each line node 1106 has a number of child machine nodes 1108 representing machines deployed on the line represented by the associated line node 1106 (e.g., Cartoner, Case Packer, Flow Wrap, Packaging System). Each machine node 1108 is associated with a number of monitored values 1110, which are data values obtained from corresponding BIDTs configured on an industrial device 302. Thomsen at [0114].
“Machines” are “hardware components” and the “monitored values” are “software components.”
Claim 17
Thomsen discloses:
wherein each asset of said industrial facility comprises an associated unique asset identifier.
The plant model 522 also defines relationships between BIDT data items associated with the respective industrial assets by assigning groups of BIDTs defined in industrial devices associated with the industrial assets to respective hierarchical elements of the plant model 522 (e.g., production lines, industrial asset identifiers, units of equipment, industrial devices, etc.). Thomsen at [0086].
Claim 18
Thomsen discloses:
wherein each asset of said industrial facility comprises an associated unique asset identifier.
The plant model 522 also defines relationships between BIDT data items associated with the respective industrial assets by assigning groups of BIDTs defined in industrial devices associated with the industrial assets to respective hierarchical elements of the plant model 522 (e.g., production lines, industrial asset identifiers, units of equipment, industrial devices, etc.). Thomsen at [0086].
Claim 19
Thomsen discloses:
wherein the common graphical representations generated by the conversions of the asset related data are stored as unified data in a central storage.
At the user level, customized applications—e.g., reporting applications, visualization applications, enterprise resource planning applications, manufacturing execution systems, etc.—can collect selected subsets of information available in industrial controllers 206 and present this information as formatted data 210 to a user in accordance with data presentation formats defined in the applications 208. Thomsen at [0068].
The “formatted data” is analogous to “unified data.”
Claim 20
Thomsen discloses:
wherein the matching of the common graphical representations is performed by a graph matching algorithm.
FIG. 22 illustrates an example methodology 2200 for aggregating asset models and using the aggregated model to generate graphical presentations of industrial data. Initially, at 2202, multiple asset models representing respective industrial assets or groups of assets are received from one or more gateway devices. As in previous examples, the asset models define groupings of BIDT data tags within hierarchical organizations of plant elements. At 2204, the asset models are integrated (e.g., at an application server system) to yield a plant model, which defines a hierarchical plant or enterprise structure comprising multiple industrial assets. Thomsen at [0147]-[0148].
Claim 26
Thomsen discloses:
wherein the generated holistic digital twin is fed back to the tools utilized to at least one of plan and operate said industrial facility to at least one of upgrade the respective tools and automatically establish crosslinks between the different tools.
Control component 3004 can be linked to predictive analysis component 512 such that supervisory program 3006 is notified when a performance issue is predicted, and supervisory program 3006 can be configured to, in response to a predicted performance issue, generate control data 3018 designed to alter control of the automation system 3008 in a manner that mitigates the predicted performance issue. Control data 3018 can include, for example, instructions to modify one or more control setpoints defined in the local industrial controller 3014, instructions to the local industrial controller 3014 to begin executing an alternate control routine, instructions to alter an operating mode of the automation system 3008, or other such instructions. In the illustrated example architecture, device interface component 2814 sends control data 3018 to the industrial controller 3014 via gateway device 3010. Thomsen at [0190].
The “generated control data” is “fed back to the tools.”
Claim 27
Thomsen discloses:
wherein the generated holistic digital twin of the industrial facility is processed to at least one of simulate and predict an operational behavior of said industrial facility.
Linking properties of an asset (automation) model 422 to corresponding properties of a non-automation model—such as a mechanical model, a business model, a thermal model, or another type of non-automation model—can yield a composite model of the industrial assets that can be used for a variety of purposes, including but not limited to holistic real-time or historical visualization of asset information, predictive analytics, simulation, training, software validation, or other such uses. Thomsen at [0151].
Claim 28
Thomsen discloses:
wherein data sources of the industrial facility providing asset related data in a data source specific data format comprise at least one of sensor assets and memory assets of said industrial facility.
Example input devices can include telemetry devices (e.g., temperature sensors, flow meters, level sensors, pressure sensors, etc.). Thomsen at [0054].
During operation of the industrial assets, industrial devices 302 monitor and control their respective industrial assets 1310, and the BIDTs 322 store data values, statuses, events or other properties that are measured and/or generated by the industrial devices 302. In this example, the contextualized BIDT data 2608 is read from the BIDTs 322 and stored in historical data storage 2606 as time-series historized data. Thomsen at [0166].
“Telemetry devices” are “sensors” and “historical data storage” is a “memory asset.”
Claim 30
Thomsen discloses:
A system for generating a holistic digital twin of an industrial facility which comprises a plurality of assets, said system comprising:
Also, one or more embodiments provide a method, comprising defining, on a system comprising a processor based on configuration input data, a digital twin that defines an industrial asset in terms of hierarchical elements… Thomsen at [0004].
a processor; and a central storage;
With reference to FIG. 38, an example environment 3810 for implementing various aspects of the aforementioned subject matter includes a computer 3812. The computer 3812 includes a processing unit 3814, a system memory 3816, and a system bus 3818. The system bus 3818 couples system components including, but not limited to, the system memory 3816 to the processing unit 3814. The processing unit 3814 can be any of various available processors. Multi-core microprocessors and other multiprocessor architectures also can be employed as the processing unit 3814. Thomsen at [0220].
The processor is configured to perform steps of a method that is substantially the same as the method disclosed in claim 15. Accordingly, for at least the same reasons and based on the same prior art as claim 15, claim 30 is rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey.
Claims 21-23 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Benesh (U.S. Pat. Pub. No. 2019/0138667).
Claim 21
Thomsen does not appear to disclose:
wherein at least one of ambiguities and mismatches occurring during the matching of the common graphical representations are resolved in response to a user input.
Benesh discloses:
wherein at least one of ambiguities and mismatches occurring during the matching of the common graphical representations are resolved in response to a user input.
The tasks can include manually matching the plan of where components are required (the plan) to where they actually are (reality) and identify mismatches. Given that this is a largely manual exercise, it imposes a high cost and a high margin of error can be present when various complex tracking systems are used together. This endeavour may be expensive, time consuming, and may still result in errors in design as it relies on significant human effort and time delays in reporting. Benesh at [0033].
Benesh is analogous art to the claimed invention because both are related to matching physical components to a virtual mapping of the components. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine Thomsen with Benesh to allow for a user to resolve ambiguities between the reality and the mappings after the mapping is completed. Motivation to combine includes developing a system that has improved accuracy by first automatically mapping the components and then, once mismatches and/or ambiguities are identified, changing the mapping based on user input, thus reducing time and effort required by the user to perform the entire process manually.
Claim 22
Thomsen does not appear to disclose:
wherein at least one of ambiguities and mismatches occurring during matching of the common graphical representations are resolved automatically based on received asset related data concerning assets affected by observed at least one of ambiguities and mismatches triggered by the graph matching algorithm in response to detected at least one of ambiguities and mismatches.
Benesh discloses:
wherein at least one of ambiguities and mismatches occurring during matching of the common graphical representations are resolved automatically based on received asset related data concerning assets affected by observed at least one of ambiguities and mismatches triggered by the graph matching algorithm in response to detected at least one of ambiguities and mismatches.
The digital twin permits a comparison of “what is” (i.e. physical reality) to “what you want” (i.e. the project design and schedule as specified by the planned data) and facilitates the generation of solutions to address mismatches between reality and planned data. In particular embodiments, the identification of mismatches between reality and planned data can be accomplished automatically by way of an automated digital verification engine. Early detection of mismatches allows construction-related issues to be resolved as they are discovered, preventing the compounding effects that can result in schedule slips, field fits, and rework. Benesh at [0037].
Claim 23
Thomsen does not appear to disclose:
wherein the graph matching algorithm is machine learned.
Benesh discloses:
wherein the graph matching algorithm is machine learned.
In other configurations, the described classification process can be carried out automatically using a machine learning (ML) system to automatically carry out the segmentation and classification of individual components and commodities within the ingested data, such as point cloud datasets. For example, computer vision techniques can be applied to segment and classify objects within a point cloud gathered and link these objects to a corresponding CAD object in a CAD file. Benesh at [0061].
It would have been obvious to use the machine learning model of Benesh to perform the mapping of Thomsen to result in a system that learns mappings via training. Motivation to combine includes generating more accurate mappings with less mismatches and ambiguities as the system learns more mappings based on continued training.
Claims 24-25 are rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Deutsch (U.S. Pat. Pub. No. 2019/0138333).
Claim 24
Thomsen does not appear to disclose:
wherein the different tools providing the asset related data comprise tools of different lifecycle stages of said industrial facility.
Deutsch, which is analogous art, discloses:
wherein the different tools providing the asset related data comprise tools of different lifecycle stages of said industrial facility.
The contextual aspect of the example embodiments derives from the further modeling of information, state and condition flows of the asset over time which provide a continual aggregation of knowledge with respect to assets and their environment throughout their lifecycle. In this way, the contextual digital twin can provide a living model that drives business outcomes. Deutsch at [0036].
Deutsch is analogous art to the claimed invention because both are related to generating digital twins. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the graphical representation of Thomsen with the lifecycle variability of Deutsch to result in a system that monitors tools throughout their lifecycle. Motivation to combine includes using the same digital twin representation throughout the lifecycle of the tools, thereby reducing time and expense of generating future mappings.
Claim 25
Thomsen discloses:
wherein the tools
FIG. 1 is a block diagram of an example industrial control environment 100. In this example, a number of industrial controllers 118 are deployed throughout an industrial plant environment to monitor and control respective industrial systems or processes relating to product manufacture, machining, motion control, batch processing, material handling, or other such industrial functions. Industrial controllers 118 typically execute respective control programs to facilitate monitoring and control of industrial devices 120 making up the controlled industrial assets or systems (e.g., industrial machines). Thomsen at [0053].
operation management tools and
In this example, an operational analytics and control system 3002 executes on a cloud platform and interfaces with the automation system 3008 via a gateway device 3010 that shares a network with the industrial controller 3014. Thomsen at [0188].
service and maintenance tools.
Thomsen does not appear to disclose:
tools of the different lifecycle stages of said industrial facility
Deutsch discloses:
tools of the different lifecycle stages of said industrial facility
The contextual aspect of the example embodiments derives from the further modeling of information, state and condition flows of the asset over time which provide a continual aggregation of knowledge with respect to assets and their environment throughout their lifecycle. In this way, the contextual digital twin can provide a living model that drives business outcomes. Deutsch at [0036].
Claim 29 is rejected under 35 U.S.C. 103 as being obvious over Thomsen in view of Hershey and Cella (U.S. Pat. Pub. No. 2019/0339687).
Claim 29
Thomsen discloses:
wherein asset related data provided in a tool specific data format of a tool comprise at least one of
text data describing the assets,
Example graphical widgets that can be supported by application server system 502 for rendering of BIDT data can include, but are not limited to, integer or real numerical displays, state or event text displays, bar graphs, line graphs, animated state machine graphics, animated graphical representations of industrial assets whose visual state is dependent on a current state, event, or value reported by a BIDT data tag, or other such widgets. Thomsen at [0121].
graphical data,
Example graphical widgets that can be supported by application server system 502 for rendering of BIDT data can include, but are not limited to, integer or real numerical displays, state or event text displays, bar graphs, line graphs, animated state machine graphics, animated graphical representations of industrial assets whose visual state is dependent on a current state, event, or value reported by a BIDT data tag, or other such widgets. Thomsen at [0121].
topological data of an asset topology and location data of asset locations.
Mechanical models 2304 define mechanical properties of the industrial asset 2302. An example mechanical model 2304 of an industrial asset 2302 may define gear ratios and/or gear diameters of a gear box used in the industrial asset 2302, types and sizes of actuators used in the industrial asset 2302, inertias and coefficients of friction of mechanical components or surfaces of the industrial asset 2302, relative locations or orientations of the mechanical components… Thomsen at [0154].
“Orientations of the mechanical components” is analogous to a “topology.”
Thomsen does not appear to disclose:
Cella, which is analogous art, discloses:
image data representing the assets,
In an embodiment, as illustrated in FIGS. 62 and 63, the signal evaluation circuit 8508 may then process the detection values to obtain information about the component or piece of equipment being monitored. Information extracted by the signal evaluation circuit 8508 may comprise rotational speed, vibrational data including amplitudes, frequencies, phase, and/or acoustical data, and/or non-phase sensor data such as temperature, humidity, image data, and the like. Cella at [0715].
acoustic data of sounds generated by the assets,
Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. In embodiments, machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states, electrical field states, capacitance states, charge and discharge states, motion, position, and many others. States may comprise combined states, where a data structure includes a series of states, each of which is represented by a place in a byte-like data structure. For example, an industrial machine may be characterized by a genetic structure, such as one that provides pressure, temperature, vibration, and acoustic data… Cella at [0400].
Cella is analogous art to the claimed invention because both are related to data collection in an industrial setting. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the disclosed sensors of Cella with the digital twin generation of Thomsen to result in a system that generates a digital twin using a more varied selection of sources. Motivation to combine includes a more robust system that is reuseable for more types of sensors and assets.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH MORRIS whose telephone number is (703)756-5735. The examiner can normally be reached M-F 8:30-5:00.
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, Ryan Pitaro can be reached at (571) 272-4071. 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.
JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188