DETAILED ACTION
This non-final rejection is responsive to the claims filed 29 June 2023. Claims 1-20 are pending. Claims 1 and 20 are independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-12, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mikkelsen (EP 2648060 A1) hereinafter known as Mikkelsen in view of Runkana (US 2020/0132882 A1) hereinafter known as Runkana.
Regarding independent claim 1, Mikkelsen teaches:
A method for monitoring a continuous industrial process, the industrial process comprising: (Mikkelsen: ¶[0018]-¶[0019]; Mikkelsen teaches monitoring a mining process.)
a number of processing stations for processing material, (Mikkelsen: Fig. 9 and ¶[0048]; Mikkelsen teaches a visualization that shows multiple production areas.)
a material flow between the number of processing stations, wherein each processing station dynamically provides data representing a state of the processing station; (Mikkelsen: Fig. 9 and ¶[0048]-¶[0049]; Mikkelsen teaches a visualization that shows multiple levels and the material flow, production areas, storage areas, etc... within the levels. Figs. 5-7 and ¶[0032], ¶[0042], and ¶[0044] teaches real-time tracking.)
the method including: providing, for each processing station, a processing station layout of the processing station, wherein the processing station layout includes: (Mikkelsen: Figs. 1, 2, 9, 10, and ¶[0048]-¶[0049]; Mikkelsen teaches a visualization that shows multiple levels and the material flow, production areas, storage areas, etc... within the levels.)
a representation of a physical layout of the processing station, and (Mikkelsen: Figs. 9-10; Mikkelsen teaches a representation of each level.)
a representation of material flow-paths to and from the processing station, wherein the processing station layout is configured for enabling a mapping of the material flow to and from the processing station; (Mikkelsen: Figs. 9-10 and ¶[0050]-¶[0052]; Mikkelsen teaches representing material flow from and to each level.)
providing, for each processing station, an interface model of the processing station, wherein the interface model includes: (Mikkelsen: Figs. 9-10; Mikkelsen teaches the visual representation of the levels and the production areas.)
a representation of data input ports and data output ports of the processing station, (Mikkelsen: Figs. 9-10; Mikkelsen teaches displaying lines running in and out of elements to represent flow.)
wherein the interface model is configured for enabling a mapping of a data flow to the data input ports and from the data output ports of the processing station; (Mikkelsen: Figs. 9-10; Mikkelsen teaches displaying lines running in and out of elements to represent flow.)
generating an information metamodel from the processing station layout and the interface model of the number of processing stations, wherein the information metamodel comprises: (Mikkelsen: ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization.)
a process layout model, the process layout model including the processing station layouts of the number of processing stations, and a process interface model, the process interface model including the interface models of the number of processing stations, and (Mikkelsen: ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization.)
...
Mikkelsen does not explicitly teach but Runkana teaches:
generating an adaptive simulation model of the industrial process by importing the data representing the state of the processing station provided by the number of processing stations into the adaptive simulation model via the information metamodel. (Runkana: Figs. 2A-2B and ¶[0023], ¶[0034], and ¶[0037]; Runkana teaches generating a simulation of a mining process using sensor data and process parameters from various stages of a mining process.)
Mikkelsen and Runkana are in the same field of endeavor as the present invention, as the references are directed to monitoring mining operations. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine generating a visualization to monitor a mining process as taught in Mikkelsen with further generating a simulation of a mining process as taught in Runkana. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Mikkelsen to include teachings of Runkana, because it would allow to optimize and update mining operations, as suggested by Runkana: ¶[0007].
Regarding claim 2, Mikkelsen in view of Runkana further teaches the method of claim 1.
Runkana further teaches:
wherein generating the adaptive simulation model further comprises selecting an appropriate simulation model from a library of simulation models. (Runkana: Figs. 2A-2B and ¶[0033]-¶[0034]; Runkana teaches basing the simulation on one of the generated ML models.)
Regarding claim 3, Mikkelsen in view of Runkana further teaches the method of claim 1.
Runkana further teaches:
wherein the method further comprises, based on the adaptive simulation model, the generating at least one of the following analytics applications: a key parameter indicator dashboard, a current operations monitor, a future operations predictor, and a what-if analysis tool. (Runkana: Figs. 2A-2B and ¶[0033]-¶[0036]; Runkana teaches receiving current values for KPIs and the plurality of process parameters of interest.)
Regarding claim 4, Mikkelsen in view of Runkana further teaches the method of claim 3.
Runkana further teaches:
wherein the method includes a feedback loop from at least one of the analytics applications to provide feedback to the information metamodel for iteratively improving at least one selected from the group consisting of: the information metamodel; and/or the simulation accuracy based on the information metamodel. (Runkana: Fig. 6; Runkana teaches a feedback loop between simulation and process parameters.)
Regarding claim 5, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the generating an information metamodel includes at least one of the following: generating a type library from the processing station layout and the interface model of the number of processing station, generating an instance model including the processing station layout of at least one of the number of processing stations as defined by the type library, generating an instance model including an interface model of at least one of the number of processing stations as defined by the type library, generating a process layout model comprising including the material flow-path to and from the at least one of the number of processing stations included in the instance model, importing data input ports for importing data representing the state of the processing station provided by the number of processing stations into the instance model, [[-]]enabling data flow between interfaces of the number of processing stations by using a data value exporter/importer linked via the instance model. (Mikkelsen: ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization.)
Regarding claim 7, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the industrial process is a mine process, the mine process including at least one processing station selected from the group consisting of: Planning, Blasting, Hauling, Storage, Ore processing, and Shipping. (Mikkelsen: ¶[0026]; Mikkelsen teaches storage being part of the mining process, as well as ore processing.)
Regarding claim 8, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the industrial process includes one selected from the group consisting of agricultural harvesting, food/beverage processing, chemical/pharmaceutical manufacturing, pulp and paper production, consumer good manufacturing, metal processing, battery production, semiconductor fabrication, land- and aircraft manufacturing. (Mikkelsen: ¶[0025]; Mikkelsen teaches production processes that include salt, grain, sand, as well as ceramic and glass materials.)
Regarding claim 9, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the interface model of each processing station is configured for providing connectivity between at least one selected from the group consisting of: the information metamodel; and the adaptive simulation model and the processing station. (Mikkelsen: Fig. 9 and ¶[0048]-¶[0049]; Mikkelsen teaches a visualization that shows multiple levels and the material flow, production areas, storage areas, etc... within the levels.)
Regarding claim 10, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the process layout model links a number of processing station layouts according to the material flow-paths between the processing stations. (Mikkelsen: Figs. 9 and 10; Mikkelsen teaches a visualization of multiple levels and their connections.)
Regarding claim 11, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the process interface model of the information metamodel links the data representing a state of the processing station provided by a processing station of the number of processing stations to the process layout model. (Mikkelsen: Figs. 9 and 10 and ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization and displaying a visualization of multiple levels and their connections.)
Regarding claim 12, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the information metamodel provides an interface for a communication of a first processing station of the number of processing stations with a second processing station of the number of processing stations. (Mikkelsen: Figs. 9 and 10 and ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization and displaying a visualization of multiple levels and their connections. The visualization shows multiple levels and processing stations within those levels.)
Regarding claim 17, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
further comprising at least one of: using the process map to identify a bottleneck in the industrial process; performing a conformance test; performing a statistical analysis; and using the statistical analysis to update the information metamodel. (Mikkelsen: ¶[0022]; Mikkelsen teaches identifying possible bottle necks.)
Regarding claim 18, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
further comprising performing a discrete-event simulation; and predicting future states of the material flow based on the discrete-event simulation. (Runkana: ¶[0023], ¶[0034], and ¶[0038]; Runkana teaches simulation models to predict key output of individual unit operations. Further, the simulation module is configured to simulate current operating conditions and can be performed on the developed models of the KPIs such as grade, recovery, power requirement, etc... The foregoing is interpreted as discrete events.)
Regarding claim 19, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen further teaches:
wherein the information metamodel and the adaptive simulation model are included in a digital twin of the industrial process. (Mikkelsen: Figs. 9 and 10 and ¶[0003]; Mikkelsen teaches gathering information about plant equipment, measurement data, etc... to generate the visualization and displaying a visualization of multiple levels and their connections. The visualization shows multiple levels and processing stations within those levels.)
Regarding claim 20, this claim recites a system that performs the method of claim 1; therefore, the same rationale for rejection applies.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mikkelsen in view of Runkana in view of Thomsen (US 2020/0265329 A1) hereinafter known as Thomsen.
Regarding claim 6, Mikkelsen in view of Runkana further teaches the method of claim 5.
Mikkelsen in view of Runkana does not explicitly teach but Thomsen teaches:
wherein the generated information metamodel is configured for allowing a dynamic addition, removal and exchange of instances within the instance model. (Thomsen: ¶[0147]; Thomsen teaches that as industrial assets are added, removed, or modified, the asset models can be reconfigured to add, remove, modify, or re-locate nodes.)
Thomsen in the same field of endeavor as the present invention, since it is directed to generating a metamodel of industrial assets. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine generating a visualization to monitor a mining process and generating adaptive simulations as taught in Mikkelsen in view of Runkana with further allowing dynamic configuration of the metamodel as taught in Thomsen. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Mikkelsen and Runkana to include teachings of Thomsen, because it would allow updating the model based on the changes to industrial assets, as suggested by Thomsen: ¶[0147].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mikkelsen in view of Runkana in view of Crouse (US 2019/0086250 A1) hereinafter known as Crouse.
Regarding claim 13, Mikkelsen in view of Runkana further teaches the method of claim 1.
Mikkelsen in view of Runkana does not explicitly teach but Crouse teaches:
further comprising: using the adaptive simulation model to identify material blobs in the material flow and to export an event log file referring to the material blobs and representing the material flow. (Crouse: Fig. 5 and ¶[0062]-¶[0075]; Crouse teaches running a fluid simulation and identifying blobs and including the information in a file.)
Crouse is analogous to the present invention, since it is reasonably pertinent to the problem faced by the inventor, i.e. logging material blobs after a simulation. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine generating a visualization to monitor a mining process and generating adaptive simulations as taught in Mikkelsen in view of Runkana with further identifying blobs in the simulation and including the information in a file as taught in Crouse. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Mikkelsen and Runkana to include teachings of Crouse, because it would allow determination if the material flow is trapped, as suggested by Crouse: ¶[0076].
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mikkelsen in view of Runkana in view of Crouse in view of Ferrar (US 2016/0292263 A1) hereinafter known as Ferrar.
Regarding claim 14, Mikkelsen in view of Runkana further teaches the method of claim 13.
Mikkelsen in view of Runkana in view of Crouse does not explicitly teach but Ferrar teaches:
further comprising: pre-processing the event log file before processing the event log file, wherein pre-processing the event log file excludes unnecessary data and/or reformats the data in the log file. (Ferrar: Fig. 3A and ¶[0062]-¶[0063]; Ferrar teaches a “parse” stage which breaks up the fields of a log entry.)
Ferrar is analogous to the present invention, since it is reasonably pertinent to the problem faced by the inventor, i.e. parsing a log. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine generating a visualization to monitor a mining process and generating adaptive simulations and further logging blob after performing a simulation as taught in Mikkelsen in view of Runkana in view of Crouse with further parsing the log the file to reformat the data as taught in Ferrar. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Mikkelsen, Runkana, and Crouse to include teachings of Ferrar, because it would allow to efficiently process the data.
Regarding claim 15, Mikkelsen in view of Runkana in view of Crouse further teaches the method of claim 13.
Mikkelsen in view of Runkana in view of Crouse does not explicitly teach but Ferrar teaches:
further comprising: processing the event log file with a process mining technique to generate a process map. (Ferrar: Fig. 3A and ¶[0062]-¶[0063]; Ferrar teaches a “normalize”, which is interpreted as reducing noise.)
Ferrar is analogous to the present invention, since it is reasonably pertinent to the problem faced by the inventor, i.e. parsing a log. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine generating a visualization to monitor a mining process and generating adaptive simulations and further logging blob after performing a simulation as taught in Mikkelsen in view of Runkana in view of Crouse with further parsing the log the file to reduce noise as taught in Ferrar. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Mikkelsen, Runkana, and Crouse to include teachings of Ferrar, because it would allow to efficiently process the data.
Regarding claim 16, Mikkelsen in view of Runkana in view of Crouse in view of Ferrar further teaches the method of claim 15.
Ferrar further teaches:
wherein processing the event log file comprises at least one of determining case identifiers required by the process mining technique, filtering, and reducing noise. (Ferrar: Fig. 3A and ¶[0062]-¶[0063]; Ferrar teaches a “normalize”, which is interpreted as reducing noise.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX OLSHANNIKOV whose telephone number is (571)270-0667. The examiner can normally be reached M-F 9:30-6.
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/ALEKSEY OLSHANNIKOV/Primary Examiner, Art Unit 2118