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 .
DETAILED ACTION
This communication is in response to: Application filed on November 16th, 2023
Claims 1-20 are pending claims.
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 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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ardis, US PG PUB# 2021/0182738A1 (hereinafter Ardis) in view of McClement US PG PUB# 2022/0291642A1 (hereinafter McClement).
As for independent claim 1:
Ardis – McClement discloses a virtual plant operator system for use in an industrial plant, the plant operator system comprising:
a data aggregator configured to monitor operating data within the industrial plant, the data aggregator being configured to send the operating data as a current state of the industrial plant; and an operator assistant configured to receive the current state, the operator assistant comprising (Ardis disclosed monitoring real plant data and feed it as a state into real world controller and learning agent retrieves a plurality of data from industrial asset in 0029-0030 and 0035):
a digital twin of the industrial plant configured to simulate plant operations in the industrial plant based on the current state (see digital twin and plant operations in 0005 and 0006),
an artificial intelligence engine having at least one machine-learned model, the machine-learned model configured to process the simulated plant operations based on the current state to determine a recommendation output (Ardis disclosed artificial intelligence engine and reinforcement model using neural network in 0031-0033);
Ardis does not disclose wherein the recommendation output comprises one or more stabilizing actions to plant operations in the industrial plant and a predicted degree of shutdown responsive to each of the stabilizing actions. McClement discloses wherein the recommendation output comprises one or more stabilizing actions to plant operations in the industrial plant and a predicted degree of shutdown responsive to each of the stabilizing actions in 0042, 0070-0073. In the cited section McClement discloses evaluating control and predicting operations along with dynamically adapting control for industrial process, also see 0041. Accordingly it would have been obvious before the effective filing date of the claimed invention to a skilled artisan to modify the system of Ardis to incorporate the reward-based evaluation of control actions and prediction of performance impact as taught by McClement, thus allow the system and user to evaluate efficient and rank performance of actions in industrial plan (McClement, 0042, 0051).
As for dependent claim 2:
Ardis – McClement discloses the plant operator system of claim 1, wherein the machine-learned model is configured to: evaluate an initial degree of shutdown based on a standard operating conditions criteria to analyze an initial state of the digital twin, execute at least one of a stabilizing action and a disrupting action to modify one or more operating variables within the digital twin, evaluate a subsequent degree of shutdown based on the standard operating conditions criteria to analyze a post-action state of the digital twin, compare the subsequent degree of shutdown to the initial degree of shutdown to determine a change in degree of shutdown within the digital twin, obtain a composite action reward based on at least the change of degree of shutdown within the digital twin, and generate the recommendation output based on the composite action reward (Ardis, 0005-0006, see digital twin and plant operations; McClement, 0042, 0070).
As for dependent claim 3:
Ardis – McClement discloses the plant operator system of claim 1, wherein the operator assistant is further configured to provide a feedback request to a plant operator, wherein the feedback request is configured to allow the plant operator to accept, reject, or modify the one or more stabilizing actions (McClement, 0039 see control inputs from the operator).
As for dependent claim 4:
Ardis – McClement discloses the plant operator system of claim 3, wherein the operator assistant is re-trained based on the feedback request (Ardis, 0033, 0046).
As for dependent claim 5:
Ardis – McClement discloses the plant operator system of claim 3, further comprising a controller configured to automatically perform at least one of the stabilizing actions accepted by the plant operator in the industrial plant (Ardis, 0033, 0046, discloses applying selected control actions to the physical plant).
As for dependent claim 6:
Ardis – McClement discloses the plant operator system of claim 5, wherein the controller is further configured to automatically perform at least one of the modified stabilizing actions modified by the plant operator in the industrial plant (Ardis, 0033, see automatic execution).
As for dependent claim 7:
Ardis – McClement discloses the plant operator system of claim 1, wherein the operator assistant is further configured to detect a cause of the destabilized scenario based on the current state (Ardis, 0029, discloses disturbance and drift detection).
As for dependent claim 8:
Ardis – McClement discloses the plant operator system of claim 1, wherein each of the one or more stabilizing actions defines an operating procedure to perform in the industrial plant (McClement, stabilizing actions in 0042).
As for dependent claim 9:
Ardis – McClement discloses the plant operator system of claim 1, wherein the operating data comprises at least one of operator actions performed in the industrial plant, trip data, process hazard risk analysis data, alarms data, historian data, and process constraint data (McClement teaches use of historical process data and operational variables (0039, 0041).
As for dependent claim 10:
Ardis – McClement discloses the plant operator system of claim 9, wherein the operator actions comprise actions performed by one or more plant operators in response to a destabilized scenario in the industrial plant (McClement, 0039, see system response and manufacturing process).
As for dependent claim 11:
Ardis – McClement discloses the plant operator system of claim 10, wherein the destabilized scenario comprises a process upset or a shutdown within the industrial plant (McClement, 0039, see system response and manufacturing process).
As for independent claim 12:
Claim 12 contains similar subject matter as claimed 1 and is rejected along the same rationale.
As for dependent claims 13, 14:
Claims 13 and 14 contains similar subject matter as claimed 7-9 and are rejected along the same rationale.
As for independent claim 15:
Claim 15 contains similar subject matter as claimed 1 and is rejected along the same rationale.
As for dependent claims 16, 17, 19, 20:
Claims 16, 17, 19, 20 contains similar subject matter as claimed 3, 7, 10, 5 and are rejected along the same rationale.
As for dependent claim 18:
Ardis – McClement discloses the method of claim 15, further comprising sending extrapolated operating data based on an extrapolated scenario of the industrial plant to the virtual plant operator system to predict one or more stabilizing actions to perform in the industrial plant in the extrapolated scenario (Ardis, see transmit recommendations and actions in 0072)..
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
The Examiner notes MPEP § 2144.01, that quotes In re Preda, 401 F.2d 825,159 USPQ 342, 344 (CCPA 1968) as stating “in considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom.” Further MPEP 2123, states that “a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID PHANTANA ANGKOOL whose telephone number is (571) 272-2673. The examiner can normally be reached M-F, 7:00-3:30 PM.
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/David Phantana-angkool/Primary Examiner, Art Unit 2172