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 .
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.
Claim(s) 1 – 7, 9 – 17, 19 – 21 and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (EP 3 065 008 A1; hereinafter “Tiwari”) in view of Storchi et al. (US 2011/0020183 A1; hereinafter “Storchi”).
Regarding claim 1, Tiwari teaches a control system (plant controller 22 and component controllers; figure 3 and 5) for optimizing a generation process performed by a generation plant, the control system comprising:
an interface unit (plant 12; controller 22; figure 1; paragraph 30) configured to be communicably coupled to the generation plant, the interface unit operable to: obtain an initial state associated with the generation plant, the initial state comprising state information indicative of a state of one or more processes of the generation plant at an initial time point;
a data contextualization unit (paragraph 30) configured to obtain contextual information regarding the initial state from a knowledge base and augment the data regarding the initial state with the contextual information;
a digital twin (model plant; figure 4; paragraphs 30, 32, 33 and 39) representative of the generation plant, the digital twin comprising a trained machine learning model configured to: receive a first state associated with the generation plant at a first time point and the associated contextual information; and predict a first future state, wherein the first future state comprises predicted state information indicative of a future state of one or more processes of the generation plant at a second time point subsequent the first time point;
an optimization unit (optimizer 64; figure 4) in communication with the interface unit and the digital twin, wherein the optimization unit is configured to: obtain an objective function which in use maps from a given state to an objective value for said given state, wherein the objective value for the given state is indicative of an estimated quantity and/or quality of generated by the one or more processes when the generation plant is in the given state; and perform an optimization process to determine an updated state from the initial state, the optimization process operable to: determine an initial future state for the initial state using the digital twin, wherein the initial future state comprises predicted state information indicative of the future state of one or more processes of the generation plant at a future time point subsequent the initial time point; and optimize the objective function to obtain the updated state such that a first objective value determined by the objective function for an updated future state is greater than a second objective value determined by the objective function for the initial future state (paragraphs 8, 72 and 74);
wherein the updated future state is determined from the updated state by the digital twin, and the updated future state comprises predicted state information indicative of the future state of one or more processes of the generation plant at the future time point (paragraphs 8, 72 and 74); and
a reasoning support unit configured to provide an evaluation of the updated future state based on the updated future state information and the contextual information (paragraph 31).
Tiwari does not specifically teach that the generation plant is a synthetic fuel generation plant.
However, Storchi teaches a synthetic fuel generation plant system for producing synthetic fuel and process control strategy (Abstract; paragraphs 15 – 36). The combination of familiar elements is likely to be obvious when it does no more than yield predictable results (see MPEP § 2143, A.). The use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious (see MPEP § 2143, C.). Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious (see MPEP § 2143, D.). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize the controller of Tiwari to optimize and control the operation of the fuel generation plant disclosed by Storchi.
Regarding claim 2, Tiwari teaches the control system of claim 1, further comprising: an action unit configured to: cause the synthetic fuel generation plant to transition to the updated state determined by the optimization process (paragraphs 8, 72 and 74).
Regarding claim 3, Tiwari teaches the control system of claim 2, wherein the action unit is further configured to: determine an action plan to transition the synthetic fuel generation plant from the initial state to the updated state (paragraphs 8, 72 and 74).
Regarding claim 4, Tiwari teaches the control system of claim 3, wherein, in order to cause the synthetic fuel generation plant to transition to the updated state, the interface unit is further configured to: provide the action plan to the synthetic fuel generation plant for execution by the synthetic fuel generation plant (paragraphs 8, 72 and 74).
Regarding claim 5, Tiwari teaches the control system of claim 3, wherein the action plan comprises a command to control an actuator of the synthetic fuel generation plant, wherein the actuator is associated with a processes of the synthetic fuel generation plant (paragraphs 8, 72 and 74).
Regarding claim 6, Tiwari teaches the control system of claim 1, wherein the state information comprises a sensor value associated with a sensor of the synthetic fuel generation plant, wherein the sensor is associated with a process of the synthetic fuel generation plant (paragraphs 16, 20 and 45).
Regarding claim 7, Tiwari teaches the control system of claim 1, wherein the trained machine learning model is a supervised machine learning model trained on a dataset of historical states, wherein the dataset of historical states is obtained from a plurality of fuel generation plants (paragraph 32).
Regarding claim 9, Tiwari teaches the control system of claim 1, wherein the action unit is further configured to: cause a remote synthetic fuel generation plant to transition to the updated state determined by the optimization process (paragraphs 8, 72 and 74).
Regarding claim 10, Tiwari teaches the control system of claim 9 wherein the action unit is further configured to: determine a remote action plan to transition the remote synthetic fuel generation plant to the updated state (paragraphs 8, 72 and 74).
Regarding claim 11, Tiwari teaches the control system of claim 10, wherein: the interface unit is further configured to be communicably coupled to the remote synthetic fuel generation plant; and in order to cause the remote synthetic fuel generation plant to transition to the updated state the interface unit is further configured to: provide the remote action plan to the remote synthetic fuel generation plant for execution by the remote synthetic fuel generation plant (figure 6).
Regarding claim 12, Tiwari teaches the control system of claim 1 wherein optimization of the objective function is constrained by a predefined constraint (paragraphs 8, 72 and 74).
Regarding claim 13, Tiwari teaches the control system of claim 12, wherein the predefined constraint in use restricts possible values of the state information of the updated state (paragraphs 8, 72 and 74).
Regarding claim 14, Tiwari teaches the control system of claim 1, wherein the reasoning support system further comprises a natural language processor configured to provide a natural language output based on the updated future state information and the contextual information (paragraphs 14, 30 and 33).
Regarding claim 15, Tiwari teaches the control system of claim 1, wherein the data contextualization unit is further configured to define an ontology for the initial state defined from said knowledge base and supplements the initial state with the ontology (paragraphs 14, 30 and 33).
Regarding claim 16, Tiwari teaches the control system of claim 15, wherein the data contextualization unit is further configured to update state information to the ontology (paragraphs 14, 30 and 33).
Regarding claim 17, Tiwari teaches the control system of claim 1, wherein the system further comprises a data transformation unit configured to remove anomalies from data, and wherein the data transformation unit is configured to remove anomalies from the data by statistical analysis (paragraphs 14, 30 and 33).
Regarding claim 19, Tiwari teaches the control system of claim 17, wherein the data transformation unit is further configured to combine multiple data sources (paragraphs 14, 30 and 33).
Regarding claim 20, Tiwari teaches the control system of claim 17, wherein the data transformation unit is further configured to identify contextual information regarding the data from pre-existing knowledge bases and add the contextual information to the data (paragraphs 14, 30 and 33).
Regarding claim 21, the same arguments above for the rejection of claim 1 in view of Tiwari and Storchi apply to claim 21. Modified Tiwari teaches the computer-implemented method utilized by the control system (plant controller 22 for plant 12; figure 1) for optimizing a fuel generation process performed by a synthetic fuel generation plant, the computer-implemented method comprising:
obtaining an initial state associated with the synthetic fuel generation plant, the initial state comprising state information indicative of a state of one or more processes of the synthetic fuel generation plant at an initial time point; determining contextual information regarding the initial state from a knowledge base and augment the data regarding the initial state with the contextual information; obtaining a digital twin representative of the synthetic fuel generation plant, the digital twin comprising a trained machine learning model which in use: receives a first state associated with the synthetic fuel generation plant at a first time point; and predicts a first future state, wherein the first future state comprises predicted state information indicative of a future state of one or more processes of the synthetic fuel generation plant at a second time point subsequent the first time point; obtaining an objective function which in use maps from a given state to an objective value for said given state, wherein the objective value for the given state is indicative of an estimated quantity and/or quality of synthetic fuel generated by the one or more processes when the synthetic fuel generation plant is in the given state; performing an optimization process to determine an updated state from the initial state, the optimization process comprising: determining an initial future state for the initial state using the digital twin, wherein the initial future state comprises predicted state information indicative of the future state of one or more processes of the synthetic fuel generation plant at a future time point subsequent the initial time point; and optimizing the objective function to obtain the updated state such that a first objective value determined by the objective function for an updated future state is greater than a second objective value determined by the objective function for the initial future state; wherein the updated future state is determined from the updated state by the digital twin, and the updated future state comprises predicted state information indicative of the future state of one or more processes of the synthetic fuel generation plant at the future time point; and providing an evaluation of the updated future state based on the updated future state information and the contextual information.
Regarding claim 39, the same arguments above for the rejection of claim 1 in view of Tiwari and Storchi apply to claim 39. Modified Tiwari teaches the a non-transitory computer readable storage medium comprising one or more program instructions which, when executed by one or more processors utilized by the control system (plant controller 22 for plant 12; figure 1), cause the one or more processors to perform the steps of: obtaining an initial state associated with a synthetic fuel generation plant, the initial state comprising state information indicative of a state of one or more processes of the synthetic fuel generation plant at an initial time point; determining contextual information regarding the initial state from a knowledge base and augment the data regarding the initial state with the contextual information; obtaining a digital twin representative of the synthetic fuel generation plant, the digital twin comprising a trained machine learning model which in use: receives a first state associated with the synthetic fuel generation plant at a first time point; and predicts a first future state, wherein the first future state comprises predicted state information indicative of a future state of one or more processes of the synthetic fuel generation plant at a second time point subsequent the first time point; obtaining an objective function which in use maps from a given state to an objective value for said given state, wherein the objective value for the given state is indicative of an estimated quantity and/or quality of synthetic fuel generated by the one or more processes when the synthetic fuel generation plant is in the given state; performing an optimization process to determine an updated state from the initial state, the optimization process comprising: determining an initial future state for the initial state using the digital twin, wherein the initial future state comprises predicted state information indicative of the future state of one or more processes of the synthetic fuel generation plant at a future time point subsequent the initial time point and optimizing the objective function to obtain the updated state such that a first objective value determined by the objective function for an updated future state is greater than a second objective value determined by the objective function for the initial future state; wherein the updated future state is determined from the updated state by the digital twin, and the updated future state comprises predicted state information indicative of the future state of one or more processes of the synthetic fuel generation plant at the future time point; and providing an evaluation of the updated future state based on the updated future state information and the contextual information.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nakabayashi et al. (US 2014/0310228 A1) teach a device and method for managing a plant model. Phan et al. (US 2022/0057786 A1) teach a site-wide operations management optimization for manufacturing and process control. Pirker et al. (US 11,454,947 B2) teach a method and apparatus for optimizing dynamically industrial production processes. Burd et al. (US 2018/0210436 A1) teaches an integrated digital twin for an industrial facility.
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BRIAN J. SINES
Primary Patent Examiner
Art Unit 1796
/BRIAN J. SINES/Primary Examiner, Art Unit 1796