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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/16/2024 and 4/2/2025 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 11 is missing from the application. Appropriate correction is required.
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 8 recites the limitation "the disruptive action" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the operator instructions" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the operator" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "the disrupting actions" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 5-10, and 12-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gooch (US PGPub 2021/0271234).
As to claim 1
Gooch discloses a computer-implemented method for training a machine-learned model (industrial plant simulation model 124, see Fig. 1) for use in an industrial plant (industrial plant 100, see Fig. 1), the method comprising:
obtaining a training variable input (training data 112, see Fig. 1) from a data store (see Fig. 1), the data store configured to store a plurality of operating variables (values of a set of industrial plant controller parameters 110, see Fig. 1) relating to plant operations in the industrial plant (see paragraph 0031, lines 4-13);
simulating the plant operations with a digital twin (simulated operation of the industrial plant) of the industrial plant (see paragraph 0031, lines 8-13; paragraph 0037, lines 1-7; and paragraph 0046, lines 1-3);
processing the training variable input with the machine-learned model (see paragraph 0038, lines 1-14) for:
evaluating an initial degree of shutdown (state of being shut down; see paragraph 0045, line 12) based on standard operating conditions criteria to analyze an initial state of the digital twin of the industrial plant (see paragraph 0045, lines 1-13);
executing at least one of a stabilizing action (control action; see paragraph 0048, line 1) and a disrupting action (equipment failure; see paragraph 0046, line 5) to modify one or more operating variables within the digital twin (see paragraph 0048, lines 1-14);
evaluating a subsequent degree of shutdown based on the standard operating conditions criteria to analyze a post-action state of the digital twin (see paragraph 0049, lines 1-9);
comparing the subsequent degree of shutdown to the initial degree of shutdown to determine a change in degree of shutdown within the digital twin (see paragraph 0050, lines 1-13); and
obtaining a composite action reward (cumulative measure of the rewards; see paragraph 0050, lines 6-7) based on at least the change of degree of shutdown within the digital twin (see paragraph 0050, lines 1-13), said composite action reward configured to reward the machine learned-model for reducing the subsequent degree of shutdown relative to the initial degree of shutdown (see paragraph 0037, lines 1-10 and paragraph 0050, lines 1-13); and
generating a prediction (probability value; see paragraph 0063, line 7) based on the composite action reward, said prediction configured for optimizing the degree of shutdown in plant operations in the industrial plant (see paragraph 0063, lines 1-14).
As to claim 2
Gooch discloses the method of claim 1, wherein the plurality of operating variables comprises operator actions data, alarms data, process behavior data, safety data and equipment design and constraints data (see paragraph 0051, lines 1-16).
As to claim 3
Gooch discloses the method of claim 1, further comprising pre-processing the operating variables within the data store based on an industrial process (see paragraph 0031, lines 1-13).
As to claim 4
Gooch discloses the method of claim 1, wherein said evaluating the degree of shutdown comprises determining the likelihood of a shutdown occurring based on operating variable values (see paragraph 0047, lines 1-5).
As to claim 7
Gooch discloses the method of claim 1, wherein said executing the stabilizing action comprises modifying one or more of the operating variables within the digital twin to maintain or reduce the degree of shutdown in the digital twin (see paragraph 0050, lines 10-13).
As to claim 8
Gooch discloses the method of claim 1, wherein said executing the disruptive action comprises modifying one or more of the operating variables within the digital twin to increase the degree of shutdown in the digital twin (see paragraph 0046, lines 1-8).
As to claim 9
Gooch discloses the method of claim 1, wherein said determining the composite action reward comprises determining a state reward to evaluate whether the degree of shutdown of the initial state and the degree of shutdown of the post-action state is the same, determining a state change reward to evaluate whether the degree of shutdown of the initial state and the degree of shutdown of the post-action state is different and determining a directional reward to evaluate whether the degree of shutdown of the post-action state is either closer to or further away from a degree of shutdown of a desired operating state than the degree of shutdown of the initial state (see paragraph 0037, lines 1-10 and paragraph 0050, lines 1-13).
As to claim 10
Gooch discloses the method of claim 1, further comprising generating operating instructions to perform in the industrial plant based on the prediction (see paragraph 0063, lines 1-14).
As to claim 12
Gooch discloses the method of claim 10, further comprising automatically performing the operator instructions in the industrial plant via a controller (industrial plant controller 102, see Fig. 1) (see paragraph 0031, lines 1-7).
As to claim 13
Gooch discloses the method of claim 10, further comprising communicating the operating instructions to the operator for the operator to perform in the industrial plant (see paragraph 0034, lines 16-20 and paragraph 0039, lines 9-11).
As to claim 14
Gooch discloses the method of claim 10, further comprising evaluating an initial degree of shutdown of an initial state of the industrial plant, monitoring operator actions performed in the industrial plant, evaluating a subsequent degree of shutdown of a post-operator-action state of the industrial plant, comparing the initial degree of shutdown to the subsequent degree of shutdown to determine a change in degree of shutdown within the industrial plant (see paragraph 0039, lines 9-11 and paragraph 0045, lines 1-13).
As to claim 15
Gooch discloses the method of claim 14, further comprising evaluating a comparison between the change of degree of shutdown within the digital twin to the change of degree of shutdown within the industrial plant and modifying one or more parameters of the machine-learned model based at least in part on the comparison to retrain the machine-learned model (see paragraph 0031, lines 1-13 and paragraph 0050, lines 1-13).
As to claim 16
Gooch discloses the method of claim 1, further comprising processing an extrapolated training variable input based on an extrapolated scenario within the industrial plant with the machine-learned model, and simulating the extrapolated scenario within the digital twin to predict a future degree of shutdown within the industrial plant and to determine a mitigating strategy for the extrapolated scenario (see paragraph 0037, lines 1-7).
Allowable Subject Matter
Claims 17-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
In regards to claim 17, Gooch discloses a system for operating an industrial plant (industrial plant 100, see Fig. 1), the system comprising:
a data store (see Fig. 1) comprising a plurality of operating variables (values of a set of industrial plant controller parameters 110, see Fig. 1) relating to plant operations within the industrial plant (see paragraph 0031, lines 4-13);
a digital twin (simulated operation of the industrial plant) of the industrial plant, wherein the digital twin is configured to simulate plant operations based on the operating variables from the data store (see paragraph 0031, lines 8-13; paragraph 0037, lines 1-7; and paragraph 0046, lines 1-3);
a machine-learned model (industrial plant simulation model 124, see Fig. 1) comprising:
a stabilizing agent configured to modify one or more of the operating variables within the digital twin to perform at least one stabilizing action (control action; see paragraph 0048, line 1) in the digital twin (see paragraph 0048, lines 1-14), wherein the stabilizing action is configured to limit a degree of shutdown (state of being shut down; see paragraph 0045, line 12), wherein the degree of shutdown is representative of a likelihood of a shutdown in the plant operations occurring based on present values of the operating variables in a given state (see paragraph 0045, lines 1-13);
a disrupting agent configured to modify one or more of the operating variables within the digital twin to perform at least one disruptive action (equipment failure; see paragraph 0046, line 5) in the digital twin, wherein the disruptive action is configured to increase the degree of shutdown (see paragraph 0046, lines 1-8);
a composite action reward (cumulative measure of the rewards; see paragraph 0050, lines 6-7) configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action- state of the digital twin (see paragraph 0050, lines 1-13), the composite action reward being further configured to penalize the machine-learned model for increasing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin (see paragraph 0037, lines 1-10 and paragraph 0050, lines 1-13);
a prediction generator configured to generate a prediction (probability value; see paragraph 0063, line 7) based on the composite action reward (see paragraph 0063, lines 1-14); and
an operating instructions generator configured to generate operating instructions based on the prediction for optimizing the plant operations (see paragraph 0063, lines 1-14).
However, Gooch fails to specifically disclose the system comprising:
an action scheduler configured to schedule either the stabilizing agent to perform the stabilizing action or the disrupting agent to perform the disruptive action.
Accordingly, independent claim 17 is allowable over the prior art. Consequently, dependent claims 18-20, which depend upon independent claim 17, are also allowable due to their dependency on claim 17.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Claims 5 and 6 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.
The following is a statement of reasons for the indication of allowable subject matter: Similar to independent claim 17; dependent claim 5 includes allowable subject matter as prior art fails to specifically teach the method further comprising determining whether to execute the stabilizing action or disruptive action based on at least one of the initial degree of shutdown of the digital twin and historical plant operating data from the industrial plant.
Accordingly, dependent claim 5 and the claim that depends upon it (claim 6) include allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115
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