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 .
Status of Claims
A reply was filed on 07/22/2026. The amendments to the claims have been entered. Claims 17 and 19-24 are pending in the application and examined herein.
Claim Rejections - 35 USC § 103
Claims 17 and 19-24 are rejected under 35 U.S.C. 103 as being unpatentable over “Development and assessment of a nearly autonomous management and control system for advanced reactors” (“Lin”) in view of US Patent No. 4,961,898 (“Bogard”) and US Publication No. 2021/0383611 (“Kanski”).
Regarding claim 17, Lin (previously cited) (see FIGS. 3-6, 9) discloses a method of monitoring a nuclear reactor, the method comprising:
receiving a signal (“sensor data,” “sensory data”) from a sensor (p. 2: “Diagnosis – Monitors safety significant factor(s) based on observed sensor data”; p. 9: “The DT-D reads sensor data from the reactor or simulator to monitor unobservable state variables”);
determining a characteristic (“state variables”) of the nuclear reactor based, at least in part, on the received signal (p. 4: “The DT-D aims to assimilate data from the operating plant to evaluate the complete states of a physical systems, including unobservable state variables, operating and fault conditions”; p. 13: “sensor data[] are processed and injected into the NAMAC diagnosis and discrepancy checker for reactor-state analysis and control”; p. 14: “NAMAC diagnosis monitors the plant state ... based on the observable sensor data”);
accessing historical information (“knowledge base”) stored in a memory, wherein the historical information is associated with a past performance of the nuclear reactor (p. 2: “Knowledge element consists of literature or information related to operating procedures and training materials, system configuration, initial conditions, reactor failure modes, experimental data, benchmarking results, etc. Data element consists of data generated by the simulation tool for development of NAMAC DTs and plant data collected from operational histories, transients, and events”; p. 3: “[]DT-D[] aims to monitor the unmeasurable and unobservable state variables by storing correlation and dependencies among different state variables”);
comparing the determined characteristic to the historical information (p. 3: “[]DT-D[] aims to monitor the unmeasurable and unobservable state variables by storing correlation and dependencies among different state variables”; p. 4: “A DT is a digital representation of a physical object or system that relies on real-time and past-history data to evaluate its complete states,” “The interrogative DT, also known as diagnosis DT, is used to interrogate the current and past histories of certain objects or systems”);
determining a condition (“complete state,” “complete condition,” “diagnosed plant state,” “system information”) of the nuclear reactor based, at least in part, on the comparison of the determined characteristic and the historical information (p. 3: “NAMAC needs to figure out the complete states of the reactor by monitoring the unobservable state variables with DT-D”; p. 4: “A DT Is a digital representation of a physical object or system that relies on real-time and past-history data to evaluate its complete states,” “The DT-D aims to assimilate data from the operating plant to evaluate the complete states of a physical systems, including unobservable state variables, operating and fault conditions”; p. 14: “NAMAC diagnoses the plant state and makes recommendations based on the inherent DTs and sensory inputs.... NAMAC diagnosis monitors the plant state ... based on the observable sensor data”);
generating a computer-generated model (“DT-D”) of at least a portion of the nuclear reactor (p. 4: “A DT is a digital representation of a physical object or system,” “The DTP contains models and tools necessary to describe and produce a virtual version that duplicates or twins the physical version”);
generating a diagnostic conclusion (“control options,” “available actions,” “control action”) associated with the nuclear reactor based on the determined condition of the nuclear reactor and the computer-generated model (FIGS. 3-4, 9, p. 2: “Strategy inventory – Identifies feasible control options based on plant state diagnosis, safety and control limits”; p. 6: “[]DT-SI[] aims to identify available actions based on the current state of the reactor (i.e., diagnosed plant state at the current time step), reactor’s safety limits, and component’s control limits”; p. 9: “The obtained values are fed to ... strategy inventory for determining available control actions”; p. 11: “The DT-SI generates control strategies based on the DT-D outputs, safety and control limits”; p. 14: “The strategy inventory provides the control procedures based on the constraints and safety significant factor”); and
autonomously taking a remedial action on the nuclear reactor based at least in part on at least one of the determined condition of the nuclear reactor or the diagnostic conclusion associated with the nuclear reactor (FIG. 12, p. 1: “Autonomous control systems are intelligent systems with self-governance ability to perform and execute control functions”; p. 2: “If the discrepancy between expected and observable states exceed a limit, an anomaly is claimed, and the operator is alerted. Meanwhile, a safety-oriented control action, i.e. SCRAM, is recommended”; p. 3: “NAMA aims to support operators’ decision-making by making recommendations ... based on real-time observations and records by DTs”; p. 14: “If no available action can be found that satisfies the safety criteria, a SCRAM signal is generated to cease the plant operation”; p. 16: “control action injected by NAMAC”; p. 17: “DTs play a critical role in NAMAC and are described as knowledge acquisition system to support different autonomous control functions”; p. 18: “alert operators for safety-minded actions”).
Lin discloses the method may be adapted to different nuclear reactor designs, systems, and scenarios (p. 3: “The NAMAC structure discussed here is highly modular. We believe that this has important advantages in scalability and interpretability, which will become more important when a much broader issue space is considered. Moreover, the modular architecture allows for a plug-and-play character such that NAMAC can be more adaptive to different reactor designs, instrument and control system, hardware platform, etc.”), but does not appear to disclose the method is specifically for monitoring structural health of reactor vessel internals.
Bogard (previously cited) is similarly directed towards a method of monitoring a nuclear reactor, specifically, towards a method of monitoring structural health of reactor vessel internals within a nuclear reactor (1:7-10, 1:63-64). Bogard teaches the method comprises:
receiving a signal from a sensor (10, 11, 26), wherein the signal is associated with a level of neutron noise emitted by the nuclear reactor (2:61-66, 3:10-14, 3:33-35);
determining a characteristic of a vibrational response of the reactor vessel internals based, at least in part, on the received signal (2:61-66; 3:7-14; 3:33-35);
accessing historical information stored in a memory, wherein the historical information is associated with past performance of the nuclear reactor (1:54-2:8; 3:36-39; 3:51-57; 4:18-22; the historical information must be inherently accessed in order to be used in the comparison);
comparing the determined characteristic to the historical information (2:4-8; 3:33-39; 3:51-57; 4:18-22);
determining a condition of the reactor vessel internals based, at least in part, on the comparison of the determined characteristic and the historical information, wherein the determined condition is indicative of the structural health of the reactor vessel internals (2:4-8; 3:39-41; 3:46-51); and
generating and displaying remedial action recommendations for one or more of the reactor vessel internals based at least in part on the determined condition of the reactor vessel internals (1:47-50; 2:39-43; 5:14-17).
Bogard further teaches monitoring the structural health of the reactor vessel internals is important for early detection of degradations of components within the nuclear reactor in order to minimize plant outages (1:12-14, 2:26-30). It would have therefore been obvious to a person having ordinary skill in the art before the effective filing date (“POSA”) to monitor reactor vessel internals in Lin’s method, as taught by Bogard, for the benefits thereof. Thus, modification of Lin in order to predict the failure of reactor vessel internal components, as suggested by Bogard, would have been obvious to a POSA.
Lin discloses the computer-generated model is a digital twin and further discloses (p. 4): “A DT is a digital representation of a physical object or system” and “The DTP contains models and tools necessary to describe and produce a virtual version that duplicates or twins the physical version.” It was well-known in the art to include a computer-generated three-dimensional model with a computer-generated digital twin. For example, Kanski (previously cited) (see FIGS. 1, 17-18, 20-21) is similarly directed towards a method of monitoring a system (164, 206) ([0002], [0088], [0092], [0098]), the method comprising receiving a signal from a sensor (214) ([0088]-[0089], [0096]-[0099], [0105]-[0106], [0128]), determining a condition of a component of the system based on the received signal and historical information ([0099], [0103], [0107]-[0109], [0131], [0135], [0138]), and generating a computer-generated digital twin (1712) of the system ([0002], [0010], [0145]-[0147]). Kanski teaches the computer-generated digital twin includes a computer-generated three-dimensional model (172, 202) of the system ([0088], [0090], [0092]-[0093], [0097], [0145]-[0152]) and a diagnostic conclusion associated with the system is generated based on the determined condition and the computer-generated digital twin with the computer-generated three-dimensional model ([0107]-[0109], [0119], [0150]-[0151]). Kanski further teaches the computer-generated three-dimensional model provides the advantages of allowing a user to view the system from multiple angles and perspectives, allowing for better and remote visualization of the system ([0073], [0084], [0093]-[0094], [0097], [0118]-[0121], [0148], [0181]). It would have therefore been obvious to a POSA, if necessary, to include a computer-generated three-dimensional model with the modified Lin’s computer-generated digital twin for the benefits thereof. Thus, further modification of Lin, if necessary, in order to enhance visualization and monitoring, as suggested by Kanski, would have been obvious to a POSA.
Regarding claim 19, Lin in view of Bogard and Kanski teaches the method of claim 17. Lin further discloses generating a prediction (“consequence factor”) associated with a future behavior of the nuclear reactor based, at least in part, on the determined condition of the reactor vessel internals and the diagnostic conclusion associated with the nuclear reactor (FIGS. 3-5, 9, p. 2: “Prognosis – Forecasts plant state for each control option”; p. 3: “Digital Twin for Prognosis (DT-P) is used to predict the short-term transient and the consequence of control actions”; p. 5: “the DT-D can be used to support predictions by DT-P, to find appropriate control actions from the operational manuals and procedures, or to directly inform operator”; p. 6: “DT-P aims to predict the future transients of state variables or lifecycles of certain components based on the past histories and current information”; p. 9: “The DT-P reads reactor information from DT-D, together with available control actions, to predict the future transients of reactor states or consequences over a certain time range”).
Regarding claim 20, Lin in view of Bogard and Kanski teaches the method of claim 17. Lin further discloses generating an alert (“alert,” “recommended action”), wherein the alert comprises a suggested maintenance plan for the reactor vessel internals based, at least in part, on the determined condition of the reactor vessel internals and the diagnostic conclusion associated with the nuclear reactor (FIGS. 3-6, 9, 12, p. 2: “If the discrepancy between expected and observable states exceed a limit, an anomaly is claimed, and the operator is alerted. Meanwhile, a safety-oriented control action, i.e. SCRAM, is recommended”; p. 3: “NAMA aims to support operators’ decision-making by making recommendations ... based on real-time observations and records by DTs”; p. 18: “alert operators for safety-minded actions”).
Regarding claim 21, Lin in view of Bogard and Kanski teaches the method of claim 17. Lin further discloses generating a digital twin (“DT-SA”) of the nuclear reactor based, at least in part, on the determined condition of the reactor vessel internals (FIGS. 3-6, 9, p. 3: “development and implementation (training and testing) of NAMAC DTs”; p. 5: “the DT-D can be used to support predictions by DT-P”; p. 6: “The DT for Strategy Assessment (DT-SA) can be described as a decision-making module that aims to rank the control actions based on a consequence factor (obtained from prognosis) and a user-defined preference structure”).
Regarding claim 22, Lin in view of Bogard and Kanski teaches the method of claim 21. Lin further discloses generating the digital twin of the nuclear reactor is further based, at least in part, on the diagnostic conclusion associated with the nuclear reactor (FIGS. 3-6, 9, p. 5: “the DT-D can be used to support predictions by DT-P”; p. 6: “The DT for Strategy Assessment (DT-SA) can be described as a decision-making module that aims to rank the control actions based on a consequence factor (obtained from prognosis) and a user-defined preference structure”; p. 9: “The DT-P reads reactor information from DT-D, together with available control actions”).
Regarding claim 23, Lin in view of Bogard and Kanski teaches the method of claim 17. Lin further discloses generating the diagnostic conclusion comprises machine learning (p. 2: “Such knowledge is extracted from the knowledge base by machine-learning algorithms”; p. 5: “constructing DT-D with machine learning and AI approaches”).
Regarding claim 24, Lin in view of Bogard and Kanski teaches the method of claim 17. Bogard further teaches determining a condition of the reactor vessel internals comprises determining an anomalous condition associated with at least a thermal shield support (2:4-8, 2:27-46, 3:39-41, 3:46-5). Thus, Lin, modified to include the reactor vessel internal monitoring as taught by Bogard and a computer-generated three-dimensional model as taught by Kanski, would have resulted in the features of claim 24.
Response to Arguments
Applicant’s amendments to the claims overcome the prior claim objections and 35 U.S.C. 112(b) rejections.
Applicant argues “Kanski’s model is, by design, a human-facing visualization tool” (Remarks, p. 7) whereas “[Lin’s] NAMAC system’s value proposition is machine-driven autonomous management, not visualization for human operators” (Remarks, p. 8). Applicant argues “[a]dding a human-facing 3D visualization interface to a system designed to minimize human involvement is antithetical to the goals of Lin” (Remarks, p. 8).
However, Lin’s system and method do not require operation “without human interaction” as asserted by Applicant (Remarks, p. 8). Rather, the cited portion of Lin discusses the correlation between the degree of autonomy (i.e., the degree of “human intervention”) and the capabilities of the system in view of uncertainty: “The degree of autonomy of an autonomous control system depends upon the extent to which it can perform fault diagnosis, planning, forecasting, and decision-making under uncertainty, without human intervention” (p. 1). Immediately following, Lin acknowledges that nuclear reactor systems in particular have a high degree of risk and uncertainty: “Owing to the inherent risk and uncertainty associated with the operation of nuclear reactor systems, the design of autonomous control systems is a challenging task” (p. 1).
Lin explicitly describes it’s system and method as a “Nearly Autonomous Management and Control (NAMAC) system designed to provide recommendations to the operator for maintaining the safety and performance of the reactor” (emphasis added) (p. 2; see also p. 17). Lin repeatedly acknowledges and emphasizes the operator (human) component of the system and method (emphasis added):
“If the discrepancy between expected and observed states exceed a limit, an anomaly is claimed, and the operator is alerted” (p. 2),
“NAMAC aims to support operators’ decision-making by making recommendations” (p. 3),
“the operator will be alerted and suggested to take safety-minded control actions.... It is stressed that NAMAC does not necessarily intend to replace traditional control and management guidelines.... NAMAC talks to the operators about what is likely to work best; it is intended to be useful whenever operators are required to monitor numerous and diverse systems in order to take the proper action” (p. 3),
“DTs can help the operator to sustain an accurate understanding of the physical system, improve the effectiveness of control, and avoid human errors” (p. 4),
“DTI usually refers to the scenarios, I/O, and user interfaces” (p. 4),
“If outputs are unobservable safety-significant state variables, the DT-D can be used ... to directly inform the operator” (p. 5),
“the factor CA,X can also be used directly by operators for deciding control actions” (p. 6),
“If the operator decides to ignore the recommendation and do nothing, the temperature will exceed the safety criteria.... If the operator decides to follow NAMAC recommendations, the maximum temperature is ... below the safety criteria” (p. 15),
“a discrepancy checker is suggested to detect unexpected reactor states and to alert operators for safety minded actions” (p. 18).
See also FIGS. 1, 3, 5. Lin’s system and method are intended to be used as a tool by operators for the intended purpose of informing operators and helping operators better understand the reactor system. There is nothing in Lin or Kanski criticizing, discrediting, or otherwise discouraging including a computer-generated three-dimensional model in order to provide enhanced visualization to an operator. Rather, such modification in order to better inform operators, as suggested by Kanski, would appear to be consistent with, as opposed to antithetical to, the goals of Lin.
Conclusion
THIS ACTION IS MADE FINAL. Prosecution on the merits is closed. See MPEP 706.07(a). 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 extension fee 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 date of this final action.
RCE Eligibility
Since prosecution is closed, this application is now eligible for a request for continued examination (RCE) under 37 CFR 1.114. Filing an RCE helps to ensure entry of an amendment to the claims, specification, and/or drawings.
Interview Information
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Contact Information
Examiner Jinney Kil can be reached at (571) 270-5217, on Monday-Thursday from 8:30AM-6:30PM ET. Supervisor Jack Keith (SPE) can be reached at (571) 272-6878.
/JINNEY KIL/Examiner, Art Unit 3646
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