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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 30, 2026, has been entered.
Claims 1, 37, and 38 are amended.
Claims 1-22, 24, and 26-38 are pending.
Information Disclosure Statement
The Information Disclosure Statement filed on April 30, 2026, has been considered.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims improve the technology of lifecycle decision strategies. See Remarks p. 3. In response, the Examiner submits that decision strategies are not a technology. Decision strategies are ineligible abstract ideas, because a decision strategy is representative of human behavior. Steps for managing personal behavior or relationships are ineligible abstract ideas, as indicated in MPEP §2106. The recited hardware in the claims is generic computer hardware that does not provide a practical application or significantly more than the recited abstract idea of predicting the evolution of damage and failure of an aging asset.
The Applicant further contends that the claims recite a practical application of any recited abstract idea. See Remarks pp. 4-5. The Examiner respectfully disagrees. Providing recommendations is not a practical application of an abstract idea. Providing recommendations is another step of managing personal behavior. The recited computer network is generic computer hardware. It is understood that computer networks enable sharing of information. The claims do not recite any apparent improvement in data formatting.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
In light of the Applicant’s amendments, the prior art rejection of claims 37 and 38 is withdrawn.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-22, 24, and 26-28 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-22, 24, and 26-28 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: predicting the evolution of damage and failure of an aging asset to provide recommendations (as evidenced by the preamble of exemplary independent claim 1), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. Additionally, note that mathematical concepts are ineligible abstract ideas. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receiving . . . data for an aging asset;” “predicting the probabilistic evolution of damage and failure time of the aging asset;” “specifying values for [a] plurality of random variable nodes;” “continuously updating the network;” “automatically generating optimal lifecycle decision strategies;” and “transmitting a recommendation alert.” The steps are all steps for managing personal behavior related to the abstract idea of predicting the evolution of damage and failure of an aging asset to provide recommendations that, when considered alone and in combination, are part of the abstract idea of predicting the evolution of damage and failure of an aging asset to provide recommendations. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of predicting the evolution of damage and failure of an aging asset. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes making a prediction regarding a physical process; and generating recommendations and alerts based on the prediction.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a communication network and graphical user interface in independent claim 1; a graphical user interface in independent claim 37; and a system with a display, input device, output device, server with a processor, and communication network in independent claim 38). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, and 26-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to Shetty (hereinafter ‘SHETTY’) in view of US 20150269490 A1 to Stillinger et al. (hereinafter ‘STILLINGER’).
Claim 1 (Currently Amended)
SHETTY discloses a probabilistic, physics-based (see ¶[0106] and [0113]; the visualization of the analytical data comprises a graph indicating corresponding probabilities of failure by the corresponding specific manner or way of failing of the failure mode for the lifetime of the physical instance of the equipment model), causal method for predicting the evolution of damage and failure time of an aging asset (see ¶[0078]-[0081]; random external events are causing mortality or failure based on the ß values. In other instances, an aging process causes parts to fail as time goes on) comprising:
receiving, over one or more communication networks (see ¶[0027] and Fig. 1; a network diagram), data for an aging asset provided to a system (see ¶[0078]; failure rate increases with time due to an aging process of parts),
wherein the data is stored in the system and comprises damage from one or more damage mechanisms, one or more flaws, failure due to one or more failure modes, or any combination thereof (see abstract and ¶[0052]; predict failure mode based on failure event data. Stress cycles),
wherein the aging asset comprises equipment in an industrial facility or plant in energy, fossil fuel, power, and manufacturing industries (see ¶[0030] and [0059]; a production environment and production processes), and
wherein the system is a dynamic, continuously learning system (see ¶[0092]; knowledge learned in training. Learn characteristics of failure) comprising operating hardware comprising a processor coupled to memory configured to provide a probabilistic, physics-based see ¶[0106] and [0113]; the visualization of the analytical data comprises a graph indicating corresponding probabilities of failure by the corresponding specific manner or way of failing of the failure mode for the lifetime of the physical instance of the equipment model), causal network (see ¶[0078]-[0081]; random external events are causing mortality or failure based on the ß values. In other instances, an aging process causes parts to fail as time goes on).
SHETTY does not specifically disclose, but STILLINGER discloses, comprising a plurality of random-variable nodes (see ¶[0005]; a Bayesian network in which random variables are represented as nodes. A probability distribution is associated with each of the nodes in the model),
SHETTY further discloses wherein the nodes represent at least one of: damage initiation time, damage state, damage rate (see ¶[0052]; stress cycles), damage causal factors, observations (see ¶[0041]; performance observed in supply chains), human expert knowledge (see ¶[0039]; human resources application can enable analysis), failure state, and failure time (see ¶[0046]; time data indicating a time of day that failure occurred);
predicting the probabilistic evolution of damage and failure time of the aging asset by applying the probabilistic physics-based causal network to the aging asset (see ¶[0052]; stress cycles).
SHETTY does not specifically disclose, but STILLINGER discloses, wherein application of the network comprises: specifying values for the plurality of random-variable nodes based on the data (see ¶[0005]; a Bayesian network in which random variables are represented as nodes. A probability distribution is associated with each of the nodes in the model),
SHETTY further discloses continuously updating the network upon receipt of real-time data for the aging asset, wherein real-time data may comprise live-streaming data from sensors, periodic data, field data, other data, or any combination thereof (see ¶[0003] and [0045]; sensor data. See also ¶[0046]; field failure rate reports);
automatically generating optimal lifecycle decision strategies based on the probabilistic evolution of damage and failure time of the aging asset predicted by the dynamic, continuously learning system, wherein optimal lifecycle decision strategies comprise designating inspection locations, designating types of inspection techniques, designating timing for inspections, designating timing for performing maintenance, designating types of maintenance to perform, designating instances when replacement is more cost-effective than maintenance, designating instances when taking no action and running the aging asset to failure is a cost-effective option, or a combination thereof (see ¶[0059]; test substantiation for new designs with minimum cost, maintenance planning and cost effective replacement strategies. See also ¶[0031]; an embodiment of the subsystem 138 connects traditional models, such as computational fluid dynamics, heat transfer, stress, and other physical models through the probabilistic model 222 with models such as manufacturing, material processing, raw material and finished part inspection, cost, and forecasting models. Simulation models that allow engineers to simulate processes such as machining, finish operations, and finish part inspections can also be used to quantify uncertainties in component performance and are also connected to the probabilistic model 222 as described herein); and
transmitting a recommendation alert, through a graphical user interface of the system, to notify users in real time of the dynamic optimal lifecycle decision strategies for the aging asset (see ¶[0059]; analysis may include recommendations to management in response to service problems).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. See ¶[0050], [0061], and [0078]. STILLINGER discloses modeling probability of failure using a physics-based functional model that correlates random variables to decision makers, such as probability of failure and failure modes. It would have been obvious for one of ordinary skill in the art at the time of invention to use the Bayesian network as taught by STILLINGER in the system executing the method of SHETTY with the motivation to use a probability of failure to generate an equipment model to estimate remaining useful life of equipment.
Claim 2 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY does not specifically disclose, but STILLINGER discloses, wherein each node in the plurality of random-variable nodes comprises one or more probabilistic states representing discrete numerical values, continuous numerical ranges, or categorical values (see ¶[0005]-[0007]; a Bayesian network in which random variables are represented as nodes. A probability distribution is associated with each of the nodes in the model. A common random variable).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. See ¶[0050], [0061], and [0078]. STILLINGER discloses modeling probability of failure using a physics-based functional model that correlates random variables to decision makers, such as probability of failure and failure modes. It would have been obvious for one of ordinary skill in the art at the time of invention to use the Bayesian network as taught by STILLINGER in the system executing the method of SHETTY with the motivation to use a probability of failure to generate an equipment model to estimate remaining useful life of equipment.
Claim 26 (Previously Presented
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY additionally discloses wherein the aging asset damage mechanisms comprise low temperature corrosion, high temperature corrosion, environmental corrosion, corrosion under insulation, contact point corrosion, microbiological corrosion, flow-induced corrosion, soil corrosion, low-cycle fatigue (see ¶[0052]; lifetimes can be measured in stress cycles), high-cycle fatigue (see again ¶[0052]; lifetimes can be measured in stress cycles), vibration fatigue, crack initiation, crack growth, stress corrosion cracking, embrittlement, fracture, metallurgical attack, creep, high temperature hydrogen attack, other mechanical damage mechanisms, other chemical damage mechanisms, other electrochemical damage mechanisms, or any combination thereof.
Claim 27 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY further discloses wherein the method further comprises extreme value analysis (EVA) methods (see ¶[0053]; Gumbel extreme value) comprising: using one or more causal methods to account for aging assets with complicated failure modes that have limited physics-based, predictive model availability (see ¶[0078]-[0083]; the parameter β indicates if there is an aging process. See also claim 1; a physical instance of the equipment model).
Claim 28 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 27.
SHETTY additionally discloses wherein the EVA methods comprise: defining a probability of failure (POF) of the aging asset in terms of an applicable EVA cumulative distribution function (CDF) (see ¶[0060], [0068], and [0093]-[0094]; the vertical axis is the cumulative distribution function);
defining a corresponding probability density function (PDF) in terms of physics-based damage causal factors (see ¶[0078]-[0081]; random external events are causing mortality or failure based on the β values. In other instances, an aging process causes parts to fail as time goes on);
updating the PDF in real-time from observations comprising field data, inspection data, maintenance data, leaks, failures, other observations, or any combination thereof and from leveraging observation data from other aging assets (see again ¶[0078]-[0081]; random external events are causing mortality or failure based on the β values. In other instances, an aging process causes parts to fail as time goes on);
using the updated PDF to predict an aging asset damage state (see ¶[0068]; define the proportion of parts that will fail up to age(t) in percent); and
using the updated CDF to predict an aging asset failure-time (see ¶[0043]; accurately estimate the probability of failure or remaining useful life of the equipment model).
Claim 29 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY additionally discloses wherein the method further comprises analytical and numerical solution procedures, or any combination thereof (see abstract and ¶[0053]; generate analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. Related quantitative models, such as the binomial, Poisson, Kaplan-Meier, Gumbel extreme value and the Crow-AMSAA, may also be used for failure analysis), wherein the analytical and numerical solution procedures are used for compilation, inference, and prediction, or any combination thereof (see abstract; predicting failure mode specific reliability characteristics of tangible equipment using parametric probability models are disclosed).
Claim 30 (Original)
The combination of SHETTY, STILLINGER, SAVELL, and TASHMAN discloses the method as set forth in claim 21.
SHETTY additionally discloses wherein the method further comprises analytical and numerical solution procedures, or any combination thereof (see abstract and ¶[0053]; generate analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. Related quantitative models, such as the binomial, Poisson, Kaplan-Meier, Gumbel extreme value and the Crow-AMSAA, may also be used for failure analysis), wherein the analytical and numerical solution procedures are used for decision strategy optimization (see ¶[0059]; test substantiation for new designs with minimum cost, maintenance planning and cost effective replacement strategies).
Claim 31 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY further discloses wherein the aging asset comprises: an insulated aging asset;
an uninsulated aging asset;
a piping system, one or more pipes, one or more piping components, or any combination thereof;
a pressure vessel, a tower, a vessel, a drum, a tank, other fixed equipment, or any combination thereof;
a heat exchanger, cooler, heater, boiler, other heat transfer equipment, or any combination thereof;
a compressor, pump, turbine, other rotating equipment, or any combination thereof (see ¶[0046]; a hydraulic pump);
a pressure relief system, pressure relief valve, pressure relief device, or any combination thereof; or
any combination thereof.
Claim(s) 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claims 1 and 2 above, and further in view of US 20200065688 A1 to Schmitz et al. (hereinafter ‘SCHMITZ’).
Claim 3 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 2.
SHETTY further discloses wherein the aging asset comprises: one or more aging components (see ¶[0083]; the age reliability relationship may be estimated based on the Weibull model for life data, which may be determined by the instantaneous failure rate, also known as the hazard function, which is useful in characterizing the failure behavior of a component, determining maintenance crew allocation, planning for spares provisioning, etc. Failure rate is denoted as failures per unit time and is mathematically represented as follows, representing the probability of an item failing via a specific failure mode at a specific time between installation and age t); and
The combination of SHETTY and STILLINGER does not specifically disclose, but SCHMITZ discloses, zero or more aging damage barriers that are used to inhibit aging of the components (see claims 1, 9, and 10; at least one blade comprises a thermal barrier coating (TBC) and blade base material, wherein the method estimates a blade-fatigue life of the blade based material on a TBC probability of failure, and wherein the TBC probability of failure is a probability of failure of the TBC. Determine a time-damage accumulation for the blade base material by combining the plurality of TBC damage scenarios and damage accumulation rules for the at least one blade.
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. SCHMITZ discloses estimating fatigue life of technical systems that includes a blade base material with damage accumulation determined by combining thermal barrier coating damage and blade damage. It would have been obvious to include the modeling as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model the probability of failure of a blade.
Claim 4 (Original)
The combination of SHETTY, STILLINGER, and SCHMITZ discloses the method as set forth in claim 3.
SHETTY does not specifically disclose, but STILLINGER discloses, wherein the aging asset, aging components, and aging damage barriers are aging due to the evolution of damage over time from one or more damage mechanisms resulting in one or more damage defects (see ¶[0031] ad [0040]; An embodiment of the subsystem 138 connects traditional models, such as computational fluid dynamics, heat transfer, stress, and other physical models through the probabilistic model 222 with models such as manufacturing, material processing, raw material and finished part inspection, cost, and forecasting models. Correlations may exist, for example, between uncertainties in blade vibratory stress, blade stress rupture life, condition of supply, and inspection capabilities).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. See ¶[0050], [0061], and [0078]. STILLINGER discloses modeling probability of failure using a physics-based functional model that includes modeling heat transfer and stress. It would have been obvious for one of ordinary skill in the art at the time of invention to use the stress modeling as taught by STILLINGER in the system executing the method of SHETTY with the motivation to generate an equipment model to estimate remaining useful life of equipment.
Claim 5 (Original)
The combination of SHETTY, STILLINGER, and SCHMITZ discloses the method as set forth in claim 4.
SHETTY does not specifically disclose, but SCHMITZ discloses, wherein the evolution of damage over time is represented by a time- dependent, spatial distribution of damage comprising one or more damage-state nodes at one or more locations on the aging components (see claims 1, 5 and 7; estimating component-fatigue life of the at least one component by determining a component-probability of failure by combining the integral of the spatial-damage accumulation and the cumulative time-damage accumulation for the at least one component based on the law of total probability).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. SCHMITZ discloses estimating fatigue life of technical systems that includes combining spatial damage and time damage to determine accumulated damage. It would have been obvious to include the spatial and time modeling as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model the probability of failure of a blade.
Claim 6 (Original)
The combination of SHETTY, STILLINGER, and SCHMITZ discloses the method as set forth in claim 5.
SHETTY does not specifically disclose, but SCHMITZ discloses, wherein time-dependent state probabilities of one or more damage- state nodes depend on one or more damage-initiation-time nodes and one or more damage-rate nodes (see abstract and ¶[0029]-[0033]; damage as a function of crack initiation and the number of cyclic loads. Njdet(x) is a fatigue crack initiation time).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. SCHMITZ discloses estimating fatigue life of technical systems that includes factoring crack initiation time. It would have been obvious to include the crack initiation time as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model the probability of failure of a blade.
Claim 7 (Original)
The combination of SHETTY, STILLINGER, and SCHMITZ discloses the method as set forth in claim 6.
SHETTY does not specifically disclose, but SCHMITZ discloses, wherein the one or more damage-initiation-time nodes and the one or more damage-rate nodes depend on zero or more damage causal factor nodes (see again ¶[0029]-[0033]; the number of cyclic loads. See also ¶[0001];thermal and mechanical loads. In case of cyclic loads, material of the technical systems may suffer from fatigue (e.g., low-cycle fatigue [LCF], high-cycle fatigue [HCF], or thermo-mechanical fatigue [TMF]).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. SCHMITZ discloses estimating fatigue life of technical systems that includes considering types of fatigue. It would have been obvious to include the fatigue types as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model the probability of failure of a blade.
Claim 8 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but SCHMITZ discloses, wherein the failure time node comprises an aging asset failure time node (see abstract; an estimation of fatigue life of a technical system), an aging component failure time node (see abstract; computing a lifing probability distribution for a component of the technical system), or an aging damage barrier failure time node (see [0002]; spallation of thermal barrier coatings may influence low-cycle fatigue failure of the blades), wherein the failure time node comprises states representing discretized time intervals with the probability of each state being the probability that failure occurs during that time interval (see ¶[0005]; the lifing probability distribution refers to probability for failure for a life number such as number of cycles).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. SCHMITZ discloses estimating fatigue life of technical systems that includes generating a lifing probability distribution that refers to a probability of failure indicating by a number of cycles for a system and its components and barriers. It would have been obvious to include the system, components, and barriers as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model failure of equipment.
Claim(s) 12 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 11796450 B1 to Maresca et al. (hereinafter ‘MARESCA’).
Claim 12 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but MARESCA discloses, wherein the damage causal factor nodes comprise: physical, mechanical, chemical, and thermodynamic properties of the aging asset, aging components, and aging damage barriers; or
physical, mechanical, chemical, and thermodynamic properties of an environment that the aging asset, aging components, and aging damage barriers are exposed to; or
planned actions that alter physical, mechanical, chemical, or thermodynamic properties of the aging asset, aging components, aging damage barriers, or a combination thereof, or
environment of the aging asset, aging components, aging damage barriers,
or a combination thereof (see claims 1-3; a probability distribution generated from corrosion data of a buried steel plate of a storage tank over a range of corrosion environments. Ensure the floor has not failed. See also col 21, ln 7-34; a tank with or without a release prevention barrier with an age determined from an equation); or
unplanned events that alter physical, mechanical, chemical, or thermodynamic properties of the aging asset, aging components, aging damage barriers, or a combination thereof, or
environment of the aging asset, aging components, aging damage barriers, or a combination thereof; or
any combination thereof.
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. MARESCA discloses determining time between inspections of tanks in corrosive environments as they age that include plated floors and release prevention barriers. It would have been obvious for one of ordinary skill in the art at the time of invention to include the tanks in a corrosive environment as taught by MARESCA in the system executing the method of SHETTY with the motivation to apply an equipment probability of failure model to tank equipment in a corrosive environment.
Claim 22 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
SHETTY does not specifically disclose, but STILLINGER discloses, wherein the method further comprises inspection effectiveness methods (see ¶[0031]; an embodiment of the subsystem 138 connects traditional models, such as computational fluid dynamics, heat transfer, stress, and other physical models through the probabilistic model 222 with models such as manufacturing, material processing, raw material and finished part inspection, cost, and forecasting models. Simulation models that allow engineers to simulate processes such as machining, finish operations, and finish part inspections can also be used to quantify uncertainties in component performance and are also connected to the probabilistic model 222 as described herein),
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful. See ¶[0050], [0061], and [0078]. STILLINGER discloses modeling probability of failure using a Bayesian physics-based functional model that includes simulating part inspections. It would have been obvious for one of ordinary skill in the art at the time of invention to include the finished part inspections as taught by STILLINGER in the system executing the method of SHETTY with the motivation to use a probability of failure to generate an equipment model to estimate remaining useful life of equipment.
The combination of SHETTY and STILLINGER does not specifically disclose, but MARESCA discloses comprising using one or more causal networks to account for measurement error, probability of detection, coverage area, or any combination thereof (see col 5, ln 60-col 6, ln 8; assessment of these results concludes that the AE method leads to correct decisions about the condition of the tank floor only 76.7% of the time with a probability of false alarm of 14.5% and a probability of missed detection of 8.8%.
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. MARESCA discloses determining time between inspections of tanks in corrosive environments as they age that includes a probability of missed detection. It would have been obvious for one of ordinary skill in the art at the time of invention to include the probability of detection as taught by MARESCA in the system executing the method of SHETTY with the motivation to apply an equipment probability of failure model to tank equipment in a corrosive environment.
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20160097698 A1 to Leao (hereinafter ‘LEAO’).
Claim 13 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not explicitly disclose, but LEAO discloses, wherein the observation nodes comprise observations of one or more damage causal factor nodes, one or more damage state nodes, or one or more failure time nodes (see abstract and ¶[0031]; historical information comprising equipment failure times can be used to obtain remaining useful life distribution.).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. See ¶[0050], [0061], and [0078]. LEAO discloses using failure times to obtain the remaining useful life of equipment. It would have been obvious to use the failure times as taught by LEAO in the system executing the method of SHETTY with the motivation to model remaining useful life.
Claim 14 (Original)
The combination of SHETTY, STILLINGER, and LEAO discloses the method as set forth in claim 13.
SHETTY further discloses wherein the observations are gathered using detection or measuring methods by a mechanical device or human, at one or more points in time (see ¶[0003] and [0045]-[0046]; solutions for predicting failures in equipment are dependent on sensor data. Data indicates the pint in time at which the failure occurred).
Claim 15 (Original)
The combination of SHETTY, STILLINGER, and LEAO discloses the method as set forth in claim 13.
SHETTY additionally discloses further comprising a time node and an uncertainty node for each observation (see ¶[0056] & [0063] and Fig. 4; interval censored data reflects uncertainty as to the exact times the units failed within an interval).
Claim(s) 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20110060568 A1 to Goldfine et al. (hereinafter ‘GOLDFINE’).
Claim 16 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but GOLDFINE discloses, wherein the human expert knowledge nodes comprise knowledge about one or more damage causal factor nodes, one or more damage state nodes, one or more damage-initiation-time nodes, one or more damage-rate nodes, or one or more failure time nodes (see ¶[0088]; the models may also account for the proximity of damage sites on a component as flaws located sufficiently near one another may have a different damage progression path than in isolation. In some embodiments, the phenomenological models may be provided and modified by one or more experts in the relevant technical arts).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. See ¶[0050], [0061], and [0078]. GOLDFINE discloses adaptive life management with probability distribution of sensor measurements (see ¶[0085]) that includes modeling by an expert to provide damage paths for damage sites. It would have been obvious for one of ordinary skill in the art at the time of invention to include the models provided by experts as taught by GOLDFINE in the system executing the method of SHETTY with the motivation to model probability of failure of equipment.
Claim 17 (Original)
The combination of SHETTY, STILLINGER, and GOLDFINE discloses the method as set forth in claim 16.
SHETTY does not specifically disclose, but GOLDFINE discloses, further comprising an error, variance, or confidence node representing a confidence in the human expert knowledge (see ¶[0088]; For example, expert knowledge may be used to estimate distribution functions for a defined number of cycles and given stress level. Uncertainty may be selected with constructive and destructive cumulative uncertainty from multiple sources such as model input, operation conditions, sensor error, ground truth errors in calibration data, recalibration data, and population data).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. See ¶[0050], [0061], and [0078]. GOLDFINE discloses adaptive life management with probability distribution of sensor measurements (see ¶[0085]) that includes modeling by an expert to provide damage paths for damage sites with cumulative uncertainty. It would have been obvious for one of ordinary skill in the art at the time of invention to include the models provided by experts as taught by GOLDFINE in the system executing the method of SHETTY with the motivation to model probability of failure of equipment.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20200273536 A1 to Aske et al. (hereinafter ‘ASKE’).
Claim 18 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but ASKE discloses, wherein the probabilistic, physics-based, causal network infers the state probabilities of nodes in the network from state probabilities set on other nodes in the network (see ¶[0056]; probability densities given by the probability distributions are directly used to infer whether given the new observation the parent node more likely has state 1 or state 2).
SHETTY discloses failure mode analysis using parametric equipment models, where a model is based on a fitting of failure event data to a continuous probability distribution (see abstract). ASKE discloses Bayesian inference using probability distributions to infer states. It would have been obvious to use the Bayesian inference as taught by ASKE in the system executing the method of SHETTY with the motivation to model equipment failure.
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20170039477 A1 to Savell et al. (hereinafter ‘SAVELL’).
Claim 19 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but SAVELL discloses, wherein the method further comprises extending the probabilistic, physics-based, causal network to comprise a plurality of decision nodes representing decisions that affect the state probabilities of random-variable nodes in the network (see abstract and ¶[0006] & [0011]; in an inference engine a conditional dependency of variables is characterized in terms of second order uncertainty to aid in improving decision making speed and precision. Mean and distribution of evidence states are utilized to provide first order uncertainties for each of a plurality of states. Performing a randomized selection of a unique value of the evidence of each state of each input variable from its distribution).
SHETTY discloses failure mode analysis using parametric equipment models, where a model is based on a fitting of failure event data to a continuous probability distribution (see abstract). SAVELL discloses probabilistic inferences in a Bayesian network for inferences and decision making using states determined by utility. It would have been obvious for one of ordinary skill in the art to use the probabilistic inferences as taught by SAVELL in the system executing the method of SHETTY with the motivation to predict the result of decisions regarding equipment.
Claim 20 (Original)
The combination of SHETTY, STILLINGER, and SAVELL discloses the method as set forth in claim 19.
SHETTY does not specifically disclose, but SAVELL discloses, wherein the extended probabilistic, physics-based, causal network comprises a plurality of utility nodes representing conditional costs and benefits of decision nodes and random-variables nodes in the network (see ¶[0005]; Utility nodes provide a value associated with a decision typically expressed in terms of money, time, safety or quality. See also ¶[0045]; utility is measured in terms of time, and the shortest time has the highest utility. Examiner Note: this implies that time is a cost).
SHETTY discloses failure mode analysis using parametric equipment models, where a model is based on a fitting of failure event data to a continuous probability distribution (see abstract). SAVELL discloses probabilistic inferences in a Bayesian network for inferences and decision making using states determined by utility. It would have been obvious for one of ordinary skill in the art to use the probabilistic inferences as taught by SAVELL in the system executing the method of SHETTY with the motivation to predict the result of decisions regarding equipment.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20050021449 A1 to Sweeney (hereinafter ‘SWEENEY’).
Claim 24 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but SWEENEY discloses, wherein the method further comprises sharing knowledge across a plurality of aging assets, from a plurality of facilities, from a plurality of industries, or any combination thereof (see ¶[0006] and [0010]; utilize the information sharing power of the Internet in the construction industry. Rate assets based on an expected remaining useful life span of the construction asset).
SHETTY discloses failure mode analysis using parametric equipment model to estimate remaining useful life of equipment (see abstract and ¶[0003]). SWEENEY discloses online management of construction assets that includes using the Internet to share information and rate items based on expected remaining life. It would have been obvious to use the Internet to share information pertaining to remaining useful life as taught by SWEENEY in the system executing the method of SHETTY with the motivation to estimate remaining useful life of equipment.
Claim(s) 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY and US 20150269490 A1 to STILLINGER et al. as applied to claim 1 above, and further in view of US 20230244836 A1 to Veronesi et al. (hereinafter ‘VERONESI’).
Claim 36 (Original)
The combination of SHETTY and STILLINGER discloses the method as set forth in claim 1.
The combination of SHETTY and STILLINGER does not specifically disclose, but VERONESI discloses, wherein the method further comprises combining probabilistic, physics-based, causal methods with statistical and data analysis methods for artificial intelligence (AI), comprising: pre-processing raw data and observations by leveraging statistical and data analysis methods for AI for classification, clustering, trending, fitting, feature extraction, other data analysis techniques, or any combination thereof (see ¶[0026]; memory 114 may include a data collector 116, a data pre-processor 118, a feature vector generator 120, a machine learning model 122, a model manager 124, a data post-processor 126, an overlay generator 128, a historical failure database 130, and a visual indicator database 132); and
using the pre-processed raw data and extracted features as inputs to the probabilistic, physics-based, causal methods (see again ¶[0026]; memory 114 may include a data collector 116, a data pre-processor 118, a feature vector generator 120, a machine learning model 122, a model manager 124, a data post-processor 126, an overlay generator 128, a historical failure database 130, and a visual indicator database 132).
SHETTY discloses failure mode analysis using parametric equipment model to estimate remaining useful life of equipment (see abstract and ¶[0003]). VERONESI discloses a system and method for pipeline modeling that uses a data pre-processor and machine learning to generate failure likelihood data (see ¶[0077]). It would have been obvious for one of ordinary skill in the art at the time of invention to include the data pre-processing as taught by VERONESI in the system executing the method of SHETTY with the motivation to determine remaining useful life of a pipeline.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY, US 20150269490 A1 to STILLINGER et al., and US 20200065688 A1 to SCHMITZ et al. as applied to claims 1-8 above, and further in view of US 10223188 B2 to Agnihotram et al. (hereinafter ‘AGNIHOTRAM’).
Claim 9 (Original)
The combination of SHETTY, STILLINGER, and SCHMITZ discloses the method as set forth in claim 8.
The combination of SHETTY, STILLINGER, and SCHMITZ does not explicitly disclose, but AGNIHOTRAM discloses, wherein the probability of failure (POF) of the aging asset, aging component, or aging damage barrier during a time interval is the probability that a failure state condition is met during the time interval, wherein the failure state condition depends on the state probabilities of one or more damage-state nodes (see col 9, ln 9-col 10 ln 35; the probability that the device is in failure state. The probability of the component in failure state and probability of device in failure state are obtained. Probabilities are determined using an expression that is a function of reliability Rs at a unit of time).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. AGNIHOTRAM discloses determining a remedy pattern for a faulty device, where probability of failure is determined based on whether a component is in a failure state. It would have been obvious for one of ordinary skill in the art at the time of invention to include the failure state determination as taught by AGNIHOTRAM in the system executing the method of SHETTY with the motivation to determine probability of failure of equipment.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY US 20150269490 A1 to STILLINGER et al., and SAVELL et al. as applied to claims 19 and20 above, and further in view of US 20200265331 A1 to Tashman et al. (hereinafter ‘TASHMAN’).
Claim 21 (Original)
The combination of SHETTY, STILLINGER, and SAVELL discloses the method as set forth in claim 20.
The combination of SHETTY, STILLINGER, and SAVELL does not specifically disclose, but TASHMAN discloses, wherein the method further comprises using the extended probabilistic, physics-based, causal network for optimizing aging asset life cycle management decision strategies for future actions by maximizing a total expected utility or a time-averaged expected utility (see abstract and ¶[0032]-[0033] & [0041]; optimize life expectancy. Use statistical inference, such as Bayesian inference using a value function which is a probability of equipment failure represented by a decision tree).
SHETTY discloses failure mode analysis using parametric equipment models, where a model is based on a fitting of failure event data to a continuous probability distribution (see abstract). TASHMAN discloses Bayesian inference using a value function to optimize life expectancy represented by a decision tree. It would have been obvious to use the decision tree as taught by TASHMAN in the system executing the method of SHETTY with the motivation to model equipment failure and optimize equipment life.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210064456 A1 to SHETTY, US 20150269490 A1 to STILLINGER et al., US 20200065688 A1 to SCHMITZ et al., and US 10223188 B2 to AGNIHOTRAM et al. as applied to claims 1-9 above, and further in view of US 20170185485 A1 to Cuddihy et al. (hereinafter ‘CUDDIHY’).
Claim 10 (Original)
The combination of SHETTY, STILLINGER, SCHMITZ, and AGNIHOTRAM discloses The method as set forth in claim 9.
The combination of SHETTY, STILLINGER, SCHMITZ, and AGNIHOTRAM does not specifically disclose, but CUDDIHY discloses, wherein the failure time of the aging asset comprises a minimum failure time selected from failure times of the aging components (see ¶[0022-[0028]; lifing calculations may be performed with Weibull curves to model failure probabilities. Times to failure of components may be simulate, and the minimum of the times may be determined. If component m has the minimum time, then the time to the first failure for the system is Tf=tm).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure. CUDDIHY discloses recursive lifing calculations, where a time to first failure is determined based on a minimum component failure time. It would have been obvious to calculate the minimum component failure time as taught by CUDDIHY in the system executing the method of SHETTY with the motivation to determine a first failure time of equipment.
Claim 11 (Original)
The combination of SHETTY, STILLINGER, SCHMITZ, AGNIHOTRAM, and CUDDIHY discloses the method as set forth in claim 9.
SHETTY does not specifically disclose, but SCHMITZ discloses, wherein the failure of the aging damage barrier influences the one or more damage-initiation time nodes and damage-rate nodes (see ¶[0030] and [0033] & claims 1, 9, and 10; at least one blade comprises a thermal barrier coating (TBC) and blade base material, wherein the method estimates a blade-fatigue life of the blade based material on a TBC probability of failure, and wherein the TBC probability of failure is a probability of failure of the TBC. Determine a time-damage accumulation for the blade base material by combining the plurality of TBC damage scenarios and damage accumulation rules for the at least one blade. Damage scenarios and cycles on regions of components are used to determine crack initiation time).
SHETTY discloses failure mode analysis using parametric equipment models, where a model may indicate a random failure indicated by a probability of failure and remaining useful life. SCHMITZ discloses estimating fatigue life of technical systems that includes a blade base material with damage accumulation determined by combining thermal barrier coating damage and blade damage. It would have been obvious to include the modeling as taught by SCHMITZ in the system executing the method of SHETTY with the motivation to model the probability of failure of a blade.
Lack of Prior Art Rejection
A thorough search was conducted, but the search did not return art that anticipates or renders obvious the limitations of independent claims 37 and 38. Those claims would be allowable if amended to overcome the rejections under 35 USC §101.
Conclusion
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
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