DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 5, 2026, has been entered.
Response to Arguments
Applicant’s response to Office action was received on August 5, 2026.
In response to Applicant’s amendment of the claims, all of the claim objections, from the previous Office action, are hereby withdrawn.
In response to Applicant’s amendment of the claims, the corresponding 101 claim rejections, from the previous Office action, are hereby withdrawn.
101 Note: Applicant’s amendments overcome the 101 claim rejections at least because: All of Applicant’s independent claims have been amended to add the element/limitation of “controlling, by the at least one processor, the manufacturing processes based on the control parameter”. Elsewhere in the independent claim(s), chemical product(s) relating to the manufacturing processes are recited. The automated control of manufacturing processes involving chemical product(s) at least implies the involvement of particular machine(s), which weighs toward eligibility. See MPEP 2106.05(b). This also at least implies physical transformation, which weighs toward eligibility here. See MPEP 2106.05(c). Furthermore, automatically carrying out a manufacturing process design, that as claimed has been designed and potentially altered toward optimization in some way, via Applicant’s independent claim limitations, amounts to integrating the abstract idea into a practical application and/or adding significantly more to the abstract idea, because such features improve manufacturing technology. See MPEP 2106.05(a).
In response to Applicant’s amendment of the claims, the corresponding prior art claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action. Note that some claims no longer have prior art rejections.
In response to Applicant’s amendment of the claims, please note the new claim rejections under 35 U.S.C. 112, below in this Office action.
In light of Examiner’s amendment of the prior art rejections, below in this Office action, Examiner believes that Applicant’s arguments, concerning the prior art rejections, are not currently applicable.
Novel/Non-Obvious Subject Matter
Examiner has determined that claims 5-6 and 11 of Applicant’s claims have overcome having prior art rejections. The reason for this is that Examiner does not believe that, at the time of Applicant’s priority date, it would have been obvious for a person of ordinary skill in the art to combine prior art disclosures to result in the particular combinations of elements/limitations in the claims, including the particular configurations of the elements/limitations with respect to each other in the particular combinations, without the use of impermissible hindsight.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, claim 17 is currently directed to a non-transitory computer-readable storage medium storing a trained neural network produced by the method according to claim 16. It currently appears that the contents of the computer-readable medium here could be interpreted as non-functional descriptive material, in which case the contents would not have patentable weight. See MPEP 2111.05(III), as well as the rejection under 35 U.S.C. 112(d), below in this Office action. Not only does this render claim 17 to be an improper dependent claim, but it does not appear that Applicant intended the contents to not have patentable weight. This renders the scope of the claim unclear, and the claim is thus rejected under 35 U.S.C. 112, indefiniteness.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 17 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 17 is directed to a non-transitory computer-readable storage medium storing a trained neural network produced by the method according to claim 16. The neural network may be stored in the format of being non-functional descriptive material, in which case its features may not have patentable weight. Because of this, the claim effectively fails to include all the limitations of the claim upon which it depends, rendering claim 17 to be an improper dependent claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
For more information, see MPEP 2111.05(III). Note that a neural network is a type of machine-learning model here. It is unclear in claim 17 if the stored neural network on the computer-readable medium is expressed in the form of executable computer program code. If the stored neural network is not in a form that is executable by a computer, then the neural network essentially is like any generic data content stored on a computer-readable medium. Thus, again, the content is treated as non-functional descriptive material and does not have patentable weight, causing the 112(d) issue.
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.
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.
Claim(s) 1-2, 4, 7-10, 12-13, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Floren, US 20220075515 A1, in view of Wikipedia, “Batch normalization,” www.wikipedia.org, version of article dated June 30, 2020, retrieved on August 23, 2025, in further view of Arjona, US 20120290267 A1, in further view of Sigfijsson, WO 2021/053084 A1.
As per Claims 1 and 9, Floren discloses:
- a computer-implemented method (or apparatus) for collaborative environmental impact optimization of manufacturing processes (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0249] (“In various implementations, simulations, optimizations, or models may be saved and/or shared with other users of the system, e.g., for review, approval, and/or implementation.”));
- receiving, by at least one processor and via a communication interface, first input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product; and second input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0013] (“In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0041] (“For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”));
- determining a first environmental impact calculation model for the first input data, the first environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data; and determining a second environmental impact calculation model for the second input data, the second environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0013] (“In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0041] (“For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”));
- connecting the first environmental impact calculation model and the second environmental impact calculation model to a connected environmental impact calculation model, wherein the step of connecting the first environmental impact calculation model and the second environmental impact calculation model to the connected environmental impact calculation model comprises mapping selected product properties as output variables from at least one upstream model based on the first environmental impact calculation model to input variables of at least one downstream model based on the second environmental impact calculation model (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”; “The objects and object properties may represent the inputs and outputs of the simulated models.”; “These panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0013] (“In some of the embodiments, the methods and systems described herein may receive input from one or more real-world systems and may also provide output to one or more real-world systems.”; “In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0034] (“FIG. 11B shows a flowchart illustrating an example process for determining relationships between one or more outputs of a first model and one or more inputs of a second model, and chaining the models together, according to one or more embodiments”); paragraph [0041] (“To satisfy the technical challenges outlined above, among others, disclosed herein are various computer systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs and outputs, running simulations, optimizing simulations, and automatically determining and implementing recommendations.”; “For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“The real-world system 102 may be a logical system, such as a representation of a supply chain.”; “In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“For example, a supply chain system may include one or more logical computations associated with the supply chain itself.”; “Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”; “For example, the artificial intelligence training system 402 may use an RNN to accurately and recurrently classify the nodes by using the nodes as training examples. The nodes may then link and/or re-link based on which classified parameter input nodes and parameter output nodes are most similar.”); paragraph [0138] (most of paragraph); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0216] (“FIG. 11B is a flowchart 1120 illustrating an example process for determining relationships between one or more parameter outputs nodes of a first model and one or more parameter inputs nodes of a second model that collectively represent a real-world system, and chaining the models together, according to one or more embodiments.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”; some of rest of paragraph));
- providing, by the at least one processor, output data of the connected environmental impact calculation model over a variable value range, wherein the step of providing output data of the connected environmental impact calculation model over the variable value range comprises restricting the variable value range to a valid and/or a predetermined variable value range (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0226] (“In various implementations, the user may select the one or more models, and one or more parameters (e.g., inputs and outputs) for the respective models. Further, the user may set certain parameter values, which may comprise constraints on the simulation/model.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0243] (“Referring to FIG. 14A, an example user interface 1400 includes many elements similar to previously discussed GUIs, and additionally includes an “optimization” panel 1402, also similar to optimizations GUIs described herein. Via panel 1402 the user may execute various scenarios/simulations, and may optimize for various goals or KPIs. For example, the user may select an objective/goal for which the system is to optimize via controls 1404, may add input variables/parameters via table 1406, and may add one or more constraints via table 1408. As described above, a column of the tables can show current/real values for the parameters, and one or more additional columns can show simulation results for the parameters. The example user interface 1400 shows optimization results that are automatically developed by the optimization component 116 (and/or other aspects of the system optimization server 104) in response to key metrics provided by a user indicating a desire to optimize revenue without going under inventory limits.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”; some of the rest of paragraph));
- calculating, by the at least one processor, a control parameter from the output data (paragraph [0007] (“The highly configurable and editable nature of the GUI allows any user the ability to visualize, simulate, and interact with the models that collectively represent a real-world system in order to assist the user in monitoring and/or controlling the real-world system by means of a continued and/or guided human-machine interaction process. In some of the embodiments the monitoring and/or controlling is performed in real-time.”); paragraph [0013] (whole paragraph); paragraph [0047] (whole paragraph); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraphs [0089]-[0090] (optimization); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraph [0198] (whole paragraph); paragraphs [0241]-[0242] (optimization); paragraph [0243] (whole paragraph); paragraph [0256] (whole paragraph));
- controlling, by the at least one processor, the manufacturing processes based on the control parameter (paragraph [0007] (“The highly configurable and editable nature of the GUI allows any user the ability to visualize, simulate, and interact with the models that collectively represent a real-world system in order to assist the user in monitoring and/or controlling the real-world system by means of a continued and/or guided human-machine interaction process. In some of the embodiments the monitoring and/or controlling is performed in real-time.”); paragraph [0013] (whole paragraph); paragraph [0017] (processors); paragraph [0041] (“To satisfy the technical challenges outlined above, among others, disclosed herein are various computer systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs and outputs, running simulations, optimizing simulations, and automatically determining and implementing recommendations.”); paragraph [0047] (whole paragraph); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraphs [0241]-[0242] (attributes); paragraph [0267] (“At block 1514, the system may implement one or more modifications/recommended actions, e.g., automatically or in response to a user input selection.”); paragraph [0275] (“At block 1534, the system may implement one or more scenarios/modifications/recommended actions, e.g., automatically or in response to a user input selection.”));
- wherein calculating the control parameter comprises calculating output data for a manufacturing process having multiple stages by calculating the overall output data across the stages (paragraph [0007] (“The highly configurable and editable nature of the GUI allows any user the ability to visualize, simulate, and interact with the models that collectively represent a real-world system in order to assist the user in monitoring and/or controlling the real-world system by means of a continued and/or guided human-machine interaction process. In some of the embodiments the monitoring and/or controlling is performed in real-time.”); paragraph [0047] (whole paragraph); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraphs [0089]-[0090] (optimization); paragraphs [0138]-[0143] (chaining of models for a process having stages); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraphs [0241]-[0242] (optimization); paragraph [0243] (whole paragraph); paragraph [0256] (whole paragraph));
- one or more computing nodes and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform (paragraph [0043] (software)).
Floren fails to disclose wherein the models are normalized. Wikipedia discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
The modified Floren fails to disclose wherein calculating output data for a manufacturing process having multiple stages comprises summing the output data for the stages across the stages. Arjona discloses wherein calculating output data for a manufacturing process having multiple stages comprises summing the output data for the stages across the stages (paragraphs [0031]-[0034] (summing emission values for bioproduct production); paragraphs [0035]-[0038] (stages); paragraph [0043] (“d) Calculation performance: Using the data selected as described before for each subtask in the corresponding formula, allows getting the total emission value expressed in g CO2 eq/MJ bioproduct, as result of adding all the stages involved.”)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that calculating output data for a manufacturing process having multiple stages comprises summing the output data for the stages across the stages, as disclosed by Arjona, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The modified Floren fails to disclose wherein a process may have output data comprising both environmental impact metrics data and product property data. Sigfijsson discloses wherein a process may have output data comprising both environmental impact metrics data and product property data (Figure 1 (a facility may have emissions of more than one substance); p. 12, lines 25-26 (description of Figure 1)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that a process may have output data comprising both environmental impact metrics data and product property data, as disclosed by Sigfijsson, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
(NOTE: These claims have been amended to recite “calculating, by the at least one processor, a control parameter from the output data, wherein calculating the control parameter comprises summing the normalized environmental impact metrics data and the product property data across the first and second normalized environmental impact calculation models”. This limitation is disclosed via a combination of Floren, Wikipedia, Arjona, and Sigfijsson. Floren discloses a computing system which chains models together and performs optimization simulations to make recommendations for control of processes. The processes may have stages that are represented by the individual models, and the processes may be manufacturing processes. While Floren describes performing the optimization simulations of the models after they have been chained together, Floren does not explicitly disclose actually summing values of the individual models that were chained together. Arjona discloses summing emissions values across multiple stages of a manufacturing process. Sigfijsson discloses that a process may have emissions of more than one substance. The purpose of Sigfijsson is that Applicant’s limitation at issue performs summing for both environmental impact metrics data and product property data. We turn to Applicant’s patent application publication for the meaning of environmental impact metrics data and product property data. Regarding environmental impact metrics data, paragraph [0029] states: “The environmental impact metrics data related to the environmental impact may indicate an environmental performance of one or more chemical products.” Paragraph [0030] states: “The environmental impact metrics data related to environmental impact may be specified or may be derived from any activity of one or more entities participating at any stage of the lifecycle of one or more product(s). The environmental impact metrics data related to the environmental impact may include one or more properties/characteristic(s) that are attributable to environmental impact of a product.” Paragraph [0030] further states: “Environmental characteristic(s) may be or may be derived from measurements taken during the lifecycle of one or more product(s). Environmental characteristics may be determined at any stage of the product lifecycle and may characterize the environmental impact of the product for such stage or up to such stage.” Paragraph [0031] states: “Environmental properties/characteristic(s) may for example include data related to carbon footprint, greenhouse gas emissions, resource usage, air emissions, ozone depletion potential, water pollution, noise pollution or eutrophication potential, biodegradability.” Therefore, emissions data may be a type of environmental impact metrics data. Regarding product property data, paragraph [0033] states: “The term product property data is to be understood broadly in the present case and comprises data related to a property of the chemical product and/or data related to the use of the chemical product.” Paragraph [0033] further states: “Data related to the use of the chemical product may include data related to further processing of the chemical product, for example by using the chemical product as reactant in further chemical reaction(s) and/or data related to the use of the chemical product, for example data related to the use of the chemical product in a treatment process and/or within a manufacturing process. Chemical product data may include chemicals data, emission data, recyclate content, bio-based content and/or production data.” Therefore, emissions data may also be a type of product property data. When Sigfijsson is used to modify the combination of Floren, Wikipedia, and Arjona, the result is that two types of emissions may be summed across the manufacturing processes; one of these emissions may be called environmental impact metrics data, and the other of these emissions may be called product property data. Wikipedia is used for the normalization. Therefore, in combination, the prior art references disclose “calculating, by the at least one processor, a control parameter from the output data, wherein calculating the control parameter comprises summing the normalized environmental impact metrics data and the product property data across the first and second normalized environmental impact calculation models”.)
As per Claim 2, Floren further discloses wherein the first and second input data relating to the at least one chemical product further comprises process data (paragraph [0041]; paragraph [0057]; paragraph [0061]; paragraph [0134]; paragraph [0136]; paragraph [0138]).
As per Claims 4 and 10, Floren further discloses wherein the input variables of at least one downstream model are controlled by a first data owner or a second data owner; and/or wherein the input variables of at least one upstream model are controlled by the first data owner or the second data owner (paragraph [0034]; paragraph [0041]; paragraph [0061]; paragraph [0064]; paragraph [0167]; paragraph [0251]).
As per Claims 7 and 12, Floren further discloses wherein the method further comprises the steps of: determining a third environmental impact calculation model for third input data based on the environmental impact metrics data and the product property data, the third environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data; and connecting the first environmental impact calculation model for the first input data and the second environmental impact calculation model for the second input data and the third environmental impact calculation model for the third input data to a connected normalized environmental impact calculation model (paragraph [0006]; paragraph [0013]; paragraph [0041]; paragraph [0061]; paragraph [0064]; paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model.”); paragraph [0145]; paragraphs [0140]-[0143]; paragraph [0175]; paragraph [0236]; paragraph [0237]; paragraph [0251]).
The modified Floren fails to disclose wherein the models are normalized. Wikipedia further discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
As per Claims 8 and 13, Floren further discloses generating, by the at least one processor, a user interface enabling evaluation of the connected environmental impact calculation model over the variable value range in terms of summing up the environmental impact metrics data and the product property data (Figure 13E; paragraph [0041]; paragraph [0047]; paragraph [0073]; paragraph [0226]; paragraph [0236]; paragraph [0237]; paragraph [0243]; paragraph [0251]; paragraph [0255]; paragraph [0259]).
The modified Floren fails to disclose wherein the models are normalized. Wikipedia further discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
As per Claim 15, Floren further discloses a non-transitory computer-readable storage medium having stored thereon instructions that, when executed on one or more computing nodes, cause the one or more computing nodes to carry out the steps of the method (paragraph [0017]; paragraph [0043]; paragraph [0284]).
As per Claim 16, Floren further discloses a neural network training method for training a neural network, the method comprising: obtaining training data; and training, using the training data, the neural network such that the trained neural network is adapted to perform the method, wherein the training uses the training data to train multivariate models in terms of costs and environmental impact reductions and product properties (paragraph [0041]; paragraph [0133]; paragraph [0135]; paragraph [0137]; paragraph [0143]; paragraph [0144]; paragraph [0153]; paragraph [0154]; paragraph [0236]; paragraph [0237]; paragraph [0243]; paragraph [0255]).
As per Claim 17, Floren further discloses a non-transitory computer-readable storage medium storing a trained neural network produced by the method (paragraph [0017]; paragraph [0133]; paragraph [0135]; paragraph [0137]; paragraph [0144]; paragraph [0153]; paragraph [0154]; paragraph [0284]).
Allowable Subject Matter
Claims 5-6 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Alhasan, US 20190338622 A1 (multi-period and dynamic long term planning optimization model for a network of gas oil separation plants).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN ERB whose telephone number is (571)272-7606. The examiner can normally be reached M - F, 11:30 AM - 8 PM.
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, JEFFREY ZIMMERMAN can be reached at (571) 272-4602. 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.
nhe
/NATHAN ERB/Primary Examiner, Art Unit 3628
Prosecution Insights