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 .
Response to Arguments
Applicant's arguments filed February 2, 2026 have been fully considered but they are not persuasive.
The applicant argues that Gooch does not explicitly teach a metric-reward mapping (Remarks of February 2, 2026, page 13). The examiner respectfully disagrees. Gooch discloses a controller receiving from an industrial plant state vectors. One of ordinary skill in the art would recognize the state vectors as the appropriate metric because the value describes the state of the industrial plant. Further, a trajectory that describes the metric-rewarding mapping as described in claim 1 is provided by the controller (¶ 34, 37 and 38).
The applicant argues that Gooch does not teach computation of a “net reward” for a time step “by cross-referencing one or more metrics in the … time step with the metric-reward mapping” (Remarks of February 2, 2026, page 14). The examiner respectfully disagrees. Gooch describes generating a “trajectory”. The trajectory consists of a current state vector, subsequent state vector, and reward. The reward is generated based on the subsequent state vector and reflects the net state of the controller as described in paragraph 38.
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, 5, 7, 9, 10 – 17, 19, 23 – 30, 34 – 38, 40, 42, and 45 - 49 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gooch (US 2020/0192340).
As per claim 1, Gooch discloses a computer-implemented method for improving anomaly/fault detection and/or mitigation in a process control plant, comprising:
receiving a metric-reward mapping including one or more metrics, each corresponding to a respective reward (¶ 37; The training system 104 determines the values of the controller parameters 110 using reinforcement learning techniques based on training data … generates “rewards” that characterize how effectively the particular control actions accomplish certain tasks); and
processing an historical plant data time series using reinforcement machine learning to train a state-action mapping (¶ 0005; Gooch discloses that training data over multiple time steps (i.e, time series) would use reinforcement machine learning for state action mappings),
wherein the processing includes computing, for at least one time step in the historical plant data time series, a net reward corresponding to the time step by cross-referencing one or more metrics in the at least one time step with the metric-reward mapping (¶ 37 and 38).
As per claim 5, Gooch discloses the computer-implemented method of claim 1, wherein processing the historical plant data time series using reinforcement machine learning to train the state- action mapping includes: training an artificial neural network to act as a function approximator for classifying an input as corresponding to one of (i) an anomaly/fault action value, and (ii) a normal action value (¶ 47).
As per claim 7, Gooch discloses the computer-implemented method of claim 1, wherein the metrics include at least one of: (i) a pre-defined statistical threshold, (ii) a pre-defined constant control limit, (iii) a set point indicator, (iv) load disturbance indicator, (v) an operating stage shift indicator; or (vi) a flaring event indicator (¶ 33).
As per claim 9, Gooch discloses the computer-implemented method of claim 1, wherein each respective reward value is expressed as an integer (¶ 38).
As per claim 10, Gooch discloses the computer-implemented method of claim 1, wherein each time step in the historical plant data time series is labeled as corresponding to one of i) a fault state or ii) a normal state (¶ 47; The predetermined set of possible events may include a “non-event” option that covers the possibility that no event affecting operation of the industrial plant occurs at the simulated time step.; The examiner argues that Gooch’s disclosure of capturing non-event data is labeling non-event (i.e., normal state) and event (i.e., fault state) data).
As per claim 11, Gooch discloses the computer-implemented method of claim 1, further comprising: preprocessing the historical plant data time series (¶ 39).
As per claim 12, Gooch discloses the computer-implemented method of claim 1, further comprising: storing at least some of the historical plant data time series in an electronic database (¶ 71).
As per claim 13, Gooch discloses the computer-implemented method of claim 1, wherein processing the historical plant data time series using reinforcement machine learning to train the state- action mapping includes: taking an action At that affects the state of an environment; receiving a reward Rt based on the action; observing the reward Rt; and updating a policy in order to maximize a cumulative reward of which the reward Rt is a part (¶ 38 - 61).
Regarding claim 14, arguments analogous to those presented for claim 1 are applicable for claim 14.
Regarding claim 15, arguments analogous to those presented for claim 7 are applicable for claim 15.
As per claim 16, Gooch discloses the computer-implemented method of claim 14, further comprising: generating the set of metrics by preprocessing process control data generated by one or more devices in a process plant (¶ 31 - 33).
As per claim 17, Gooch discloses the computer-implemented method of claim 16, wherein the one or more devices in the process plant include at least one of: a sensor, a valve, a transmitter, a positioner, a standard 4-20 mA device, a field device, a HART@ device, a Foundation@ Fieldbus device, a Profibus device, a DeviceNet device, a ControlNet device; or a Modbus device (¶ 33 and 61).
Regarding claim 19, arguments analogous to those presented for claim 5 are applicable for claim 19.
As per claim 23, Gooch discloses the computer-implemented method of claim 14, wherein the remedial action is an active remedial action (¶ 34).
As per claim 24, Gooch discloses the computer-implemented method of claim 23, wherein the active remedial action includes at least one of (i) actuating a valve, (ii) causing a stack to release a gas flare, or (iii) performing an action with respect to the plant (¶ 34).
Regarding claim 25, arguments analogous to those presented for claim 1 are applicable for claim 25.
Regarding claim 26, arguments analogous to those presented for claim 7 are applicable for claim 26.
Regarding claim 27, arguments analogous to those presented for claim 16 are applicable for claim 27.
Regarding claim 28, arguments analogous to those presented for claim 17 are applicable for claim 28.
Regarding claim 30, arguments analogous to those presented for claim 5 are applicable for claim 30.
Regarding claim 34, arguments analogous to those presented for claim 23 are applicable for claim 34.
Regarding claim 35, arguments analogous to those presented for claim 24 are applicable for claim 35.
Regarding claim 36, arguments analogous to those presented for claim 24 are applicable for claim 36.
Regarding claim 37, arguments analogous to those presented for claim 17 are applicable for claim 37.
As per claim 38, Gooch discloses the one or more data generation devices of claim 36, wherein are further configured to: generate the data corresponding to the ongoing industrial control process of the process control system in response to receiving an activation instruction from a remote anomaly/detection application (¶ 34 and 35).
Regarding claim 40, arguments analogous to those presented for claim 5 are applicable for claim 40.
Regarding claim 42, arguments analogous to those presented for claim 1 are applicable for claim 42.
Regarding claim 45, arguments analogous to those presented for claim 23 are applicable for claim 45.
Regarding claim 46, arguments analogous to those presented for claim 24 are applicable for claim 46.
As per claim 47, Gooch discloses the one or more data generation devices of claim 42, wherein the remedial action includes causing at least one of the one or more data generation devices to stop generating the data corresponding to the ongoing industrial control process of a process control system (¶ 35).
As per claim 48, Gooch discloses the one or more data generation devices of claim 36, further configured to: transmit the generated data to one or both of (i) another data generation device, and (iii) a remote anomaly/fault detection device (¶ 35).
As per claim 49, Gooch discloses the one or more data generation devices of claim 48, further configured to: receive, in response to the transmitting, updated reinforcement-learned information (¶ 35 and 62).
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.
Claim(s) 2, 3, 18, 29, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gooch in view of Chamie et al (US 2021/0294304, hereafter Chamie).
As per claim 2, Gooch discloses the computer-implemented method of claim 1.
However, Gooch does not explicitly teach wherein processing the historical plant data time series using reinforcement machine learning to train the state- action mapping includes: generating a Q-table including a plurality of states, each state having a respective anomaly/fault action value and a respective normal action value.
In the same field of endeavor, Chamie teaches wherein processing the historical plant data time series using reinforcement machine learning to train the state- action mapping includes: generating a Q-table including a plurality of states, each state having a respective anomaly/fault action value and a respective normal action value (¶ 20 - 23).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Gooch in view of Chamie. The advantage is boosting computation speed.
As per claim 3, Gooch discloses the computer-implemented method of claim 2.
However, Gooch does not explicitly teach wherein a cardinality of the Q-table is defined via the rule of product with respect to a number of possible different rewards for each of the metrics.
In the same field of endeavor, Chamie teaches wherein a cardinality of the Q-table is defined via the rule of product with respect to a number of possible different rewards for each of the metrics (¶ 20 - 23).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Gooch in view of Chamie. The advantage is boosting computation speed.
Regarding claim 18, arguments analogous to those presented for claim 2 are applicable for claim 18.
Regarding claim 29, arguments analogous to those presented for claim 2 are applicable for claim 29.
Regarding claim 39, arguments analogous to those presented for claim 2 are applicable for claim 39.
Claim(s) 6, 20, 31, and 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gooch in view of Cohen et al (US 2019/0236447, hereafter Cohen).
As per claim 6, Gooch discloses the computer-implemented method of claim 5.
However, Gooch does not explicitly teach wherein the artificial neural network is a recurrent neural network.
In the same field of endeavor, Cohen teaches wherein the artificial neural network is a recurrent neural network (¶ 49).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Gooch in view of Cohen. The advantage is being able to operate at an economic optimum.
Regarding claim 20, arguments analogous to those presented for claim 6 are applicable for claim 20.
Regarding claim 31, arguments analogous to those presented for claim 6 are applicable for claim 31.
Regarding claim 41, arguments analogous to those presented for claim 6 are applicable for claim 41.
Claim(s) 21, 22, 32, 33, 43, and 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gooch in view of Hubler et al (US 2022/0357709, hereafter Hubler).
As per claim 21, Gooch discloses The computer-implemented method of claim 14.
However, Gooch does not explicitly teach wherein the remedial action is a passive remedial action.
In the same field of endeavor, teaches wherein the remedial action is a passive remedial action (¶ 31).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Gooch in view of Hubler. The advantage is a cost effective and automated system.
As per claim 22, Gooch discloses the computer-implemented method of claim 21.
However, Gooch does not explicitly teach wherein the passive remedial action includes at least one of (i) sounding an alarm, (ii) transmitting a notification, or (iii) displaying an alert.
In the same field of endeavor, Hubler teaches wherein the passive remedial action includes at least one of (i) sounding an alarm, (ii) transmitting a notification, or (iii) displaying an alert (¶ 31).
Regarding claim 32, arguments analogous to those presented for claim 21 are applicable for claim 32.
Regarding claim 33, arguments analogous to those presented for claim 22 are applicable for claim 33.
Regarding claim 43, arguments analogous to those presented for claim 21 are applicable for claim 43.
Regarding claim 44, arguments analogous to those presented for claim 22 are applicable for claim 44.
Allowable Subject Matter
Claim(s) 4 and 8 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIKAODILI E ANYIKIRE whose telephone number is (571)270-1445. The examiner can normally be reached 8 am - 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Czekaj can be reached at 571-272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487