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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims recite “plant operator system” with no physical hardware in one of the limitations and considered software per se. The Office suggests adding a physical hardware in one of the limitations.
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 PB PUB# 2022/0291642A1 (hereinafter McClement).
As for independent claim 1:
Ardis 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 (0041). Accordingly it would have been obvious before the effective filing date of the claimed invention to a skilled artisan to modify the plan operating system of Ardis to incorporate the dynamically control industrial process and prediction as taught by McClement, thus allow automatic controls of stabilizing actions within the desired parameters (McClement, 0046-0047).
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 (Ardis, 0031, 0044, discloses initial performance and evaluation), 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 (See digital twin as taught by Ardis in 0005-0007, 0019 and reward evaluation and reward function in McClement 00045, 0070-0072).
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 (Ardis discloses operator adjust parameters and automatically generate recommendation results in 0057; McClement deep reinforcement learning in 0006).
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 (McClement discloses reinforcement learning and embedding neural network in 0006-0007, thus receiving feedback request).
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, 00033 and 0046, see automatic execution for control actions).
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, 0029, 0033 see operator, learning agent; McClement 0006, input reinforcement learning).
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, learning agent and operator in 0029).
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 (Ardis, 00033 and 0046, see automatic execution for control actions).
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 discloses historical data and operator actions in 0039).
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 (Ardis, 0024, 0029).
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 (Ardis disclosed adjusting operating parameters in 0030).
As for independent claim 12:
Ardis 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 operating data including at least one of extrapolated operating data from an extrapolated scenario, operator actions performed in the industrial plant, and trip data, the data aggregator is configured to send the operating data from a future destabilized scenario based on the extrapolated operating data as a current state (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):
and an operator assistant configured to receive the current state, the operator assistant comprising: a digital twin of the industrial plant configured to simulate plant operations in the industrial plant based on the current state, an artificial intelligence engine having at least one machine-learned model (see digital twin and plant operations in 0005 and 0006), 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 in the future destabilized scenario and a predicted degree of shutdown for each of the stabilizing actions. McClement wherein the recommendation output comprises one or more stabilizing actions to plant operations in the industrial plant in the future destabilized scenario and a predicted degree of shutdown for 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 (0041). Accordingly it would have been obvious before the effective filing date of the claimed invention to a skilled artisan to modify the plan operating system of Ardis to incorporate the dynamically control industrial process and prediction as taught by McClement, thus allow automatic controls of stabilizing actions within the desired parameters (McClement, 0046-0047).
As for dependent claim 13:
Ardis – McClement discloses the plant operator system of claim 12, wherein the operator assistant is configured to detect a cause of the future destabilized scenario based on the current state and provide the cause to a plant operator (Ardis disclosed adjusting operating parameters in 0030).
As for dependent claim 14:
Ardis – McClement discloses the plant operator system of claim 12, further comprising at least one controller and wherein the operator assistant is configured to forecast the destabilized future scenario within the industrial plant and perform a preventative action via the controller to prevent the destabilized future scenario from occurring within the industrial plant (McClement discloses evaluating control and predicting operations along with dynamically adapting control for industrial process (0041).
As for independent claim 15:
Ardis discloses a method of operating an industrial plant, the method comprising:
monitoring operating data within the industrial plant; sending a current state based on the operating data to an operator assistant of a virtual plant operator system; identifying a destabilized scenario within the industrial plant via the operator assistant; updating a digital twin of the operator assistant based on the current state to simulate plant operations in the industrial plant based thereon (Ardis disclosed artificial intelligence engine and reinforcement model using neural network in 0031-0033);
processing the current state within an artificial intelligence engine of the operator assistant, wherein processing comprises: evaluating an initial degree of shutdown based on standard operating conditions criteria to analyze an initial state of the digital twin; executing at least one of a stabilizing action and a disrupting action to modify one or more operating variables within the digital twin; evaluating a subsequent degree of shutdown based on the standard operating conditions criteria to analyze a post-action state of the digital twin; comparing the subsequent degree of shutdown to the initial degree of shutdown to determine a change in degree of shutdown within the digital twin (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):see digital twin and plant operations in 0005 and 0006), and
Ardis does not disclose obtaining a composite action reward based on at least the change of degree of shutdown within the digital twin, the composite action reward configured to reward the machine-learned model for reducing the subsequent degree of shutdown relative to the initial degree of shutdown; recommending one or more stabilizing actions based on the composite action reward to perform in the industrial plant; and providing a predicted degree of shutdown for each stabilizing action. McClement obtaining a composite action reward based on at least the change of degree of shutdown within the digital twin, the composite action reward configured to reward the machine-learned model for reducing the subsequent degree of shutdown relative to the initial degree of shutdown; recommending one or more stabilizing actions based on the composite action reward to perform in the industrial plant; and providing a predicted degree of shutdown for each stabilizing action in 0042, 0045, 0070-0072. In the cited section McClement discloses evaluating control and predicting operations along with dynamically adapting control for industrial process along with reward evaluation and reward function in 0041 and 0045. Accordingly it would have been obvious before the effective filing date of the claimed invention to a skilled artisan to modify the plan operating system of Ardis to incorporate the dynamically control industrial process and prediction as taught by McClement, thus allow automatic controls of stabilizing actions within the desired parameters (McClement, 0046-0047).
As for dependent claim 16:
Ardis – McClement discloses the method of claim 15, further comprising detecting a cause of the destabilized scenario via the operator assistant and providing the cause to a plant operator (Ardis disclosed adjusting operating parameters in 0030).
As for dependent claim 17:
Ardis – McClement discloses the method of claim 15, further comprising providing 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 recommended by the operator assistant (Ardis discloses operator adjust parameters and automatically generate recommendation results in 0057; McClement deep reinforcement learning in 0006).
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 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).
As for dependent claim 19:
Ardis – McClement discloses the method of claim 15, further comprising capturing an operator action performed in response to a destabilized scenario within the industrial plant (Ardis, 0024, 0029).
As for dependent claim 20:
Ardis – McClement discloses the method of claim 15, further comprising automatically performing the stabilizing action within the industrial plant via a controller of the virtual plant operator system (Ardis disclosed monitoring real plant data, learning agent retrieves a plurality of data from industrial asset in 0029-0030 and 0035):
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