DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is in response to the Applicant’s submission filed 16 December 2025 [hereinafter Response], where:
Claims 1-7 have been cancelled by Preliminary Amendment filed 27 December 2022.
Claims 15, 16, and 17 have been amended.
Claims 8-20 are pending.
Claims 8-20 are rejected.
Foreign priority is claimed to EP20183283, filed 30 June 2020. A certified copy of this paper has been filed 27 December 2022. Accordingly, receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections – 35 U.S.C. § 112
3. 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.
4. The rejection to claims 15 and 19 under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention is WITHDRAWN.
Claim Rejections - 35 U.S.C. § 101
5. 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.
6. Claims 8-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 8 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(b.1)]1 performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant.” The activity of “[(b.1)] performing a Bayesian inference” is performing a statistical inference, as well as providing a way to properly update beliefs as new observations are made, which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is thus a mental process, (MPEP § 2106.04(a)(2) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Also, the plain meaning of “[(b.1)] performing a Bayesian inference” is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is thus a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is also one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 8 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites the “[(a)] creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] entering a diagnosis mode of the model,” which is using the generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(h)). Therefore, claim 8 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites the “[(a)] creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] entering a diagnosis mode of the model,” which is using the generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not amount to significantly more than the abstract idea. (MPEP § 2106.05(h)). Therefore, claim 8 is subject-matter ineligible.
Claim 9 depends from claim 8. The claim recites more details or specifics of the additional element of the “[(a)] creating an inference model,” in that “[(a.1)] wherein the Bayesian inference of the inference model is performed during a training mode of the inference model utilizing measurement data from the engineering plant.” That is, “during a training mode of the inference model” and “utilizing measurement data” are more details or specifics to the use of a generic computer component (inference model), and accordingly, is merely more specific to the additional element. The claim also recites more details or specifics to the “inference model” in that “[(a.2)] wherein estimates of model parameters representing priors in the inference model are optimized,” which directed to the “training mode of the inference model,” and accordingly, is merely more specific to the additional element. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claim 9 is subject-matter ineligible.
Claims 10 and 11 depend directly or indirectly from claim 8. The claims recite more details or specifics to the additional element of “[(a)] creating an inference model,” in that “wherein the inference model is created using a bond graph from the engineering information,” and accordingly, is merely more specific to the additional element. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claims 10 and 11 are each subject-matter ineligible.
Claims 12, 13, and 14 depend directly or indirectly from claim 8. The claims recite the limitation of “[(c)] wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library which contains code to be generated and inference variables for each component type in the engineering plant for which Bayesian inference is to be performed.” Under Step 2A Prong Two, the plain meaning of “generate a metamodel” is to define the structure, rules, and constraints for particular modeling language to generate a model of a model, which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is thus a mental process, (MPEP § 2106.04(a)(2) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claims also recite more details or specifics of the abstract idea of “[(a)] creating an inference model,” in that “[(a.1)] wherein the inference model of the engineering plant is created from the metamodel,” and accordingly, is merely more specific to the additional element. . The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claims 12, 13, and 14 are each subject-matter ineligible.
Claims 15, 16, and 17 depend directly or indirectly from claim 8. The claims recite more details or specifics to the additional element of “[(a)] creating an inference model,” in that “[(a.1)] wherein the engineering information on the engineering plant comprises a piping and instrumentation (P&I) flow diagram,” and accordingly, are merely more specific to the abstract idea. Therefore, claims 15, 16, and 17 are each subject-matter ineligible.
Claim 18 recites a system, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitation of “[(b)] an inference module which utilizes measurement data from the engineering plant to perform Bayesian inference of fault probabilities in a diagnosis mode of the inference model.” The activity of “[(b)] . . . utilizes measurement data from the engineering plant to perform Bayesian inference of fault probabilities” is performing a statistical inference, as well as providing a way to properly update beliefs as new observations are made, which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is thus a mental process, (MPEP § 2106.04(a)(2) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Also, the plain meaning of “[(b)] to perform a Bayesian inference” is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is thus a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is also one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 18 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a processor,” “memory,” “a transformation module,” and “an inference module,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), and do not serve to integrate the abstract idea into a practical application. The claim also recites additional elements of “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites the additional element to “[(a)] a transformation module creates an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] an inference module . . . . entering a diagnosis mode of the inference model,” which is using the generic computer components (processor, memory, transformation module, inference module, inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(h)). Therefore, claim 18 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “a processor,” “memory,” “a transformation module,” and “an inference module,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), and do not amount to significantly more than the abstract idea. The claim also recites additional elements of “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites the additional element to “[(a)] a transformation module creates an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] an inference module . . . . entering a diagnosis mode of the inference model,” which is using the generic computer components (processor, memory, transformation module, inference module, inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not amount to significantly more than the abstract idea. (MPEP § 2106.05(h)). Therefore, claim 18 is directed to the abstract idea.
Claim 19 depends from claim 18. The claim recites more details or specifics of the abstract idea of “[(b)] . . . to perform Bayesian inference,” in that “[(b.1)] wherein the inference module is further configured to perform Bayesian inference of the inference model,” and accordingly, is merely more specific to the abstract idea. Also, the claim recites more details or specifics of the additional element of “[(a)] . . . creates an inference model,” in that “[(a.1)] wherein estimates of model parameters representing priors in the inference model are optimized,” and accordingly, is merely more specific to the additional element. . The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claim 19 is subject-matter ineligible.
Claim 20 recites a “computer program product,” which is an article of manufacture, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites, by the incorporation of claim 8, the limitations of “[(b.1)] performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant.” The activity of “[(b.1)] performing a Bayesian inference” is performing a statistical inference, as well as providing a way to properly update beliefs as new observations are made, which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is thus a mental process, (MPEP § 2106.04(a)(2) sub III), being one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Also, the plain meaning of “[(b.1)] performing a Bayesian inference” is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is thus a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is also one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 20 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “memory of a computer and which comprises software code sections . . . executed when the computer program product is executed on the computer,” which is the use of generic computer components (memory, computer, software code sections) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites the additional element of “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites the “[(a)] creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] entering a diagnosis mode of the model,” which is using the generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(h)). Therefore, claim 20 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “memory of a computer and which comprises software code sections . . . executed when the computer program product is executed on the computer,” which is the use of generic computer components (memory, computer, software code sections) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites the additional element of “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites the “[(a)] creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables,” and “[(b)] entering a diagnosis mode of the model,” which is using the generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not amount to significantly more than the abstract idea. (MPEP § 2106.05(h)). Therefore, claim 20 is subject-matter ineligible.
Claim Rejections - 35 U.S.C. § 102
7. 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.
8. Claims 8, 18, and 20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by US Published Application 20090204234 to Susteata et al. [hereinafter Susteata].
Regarding claim 8, Susteata teaches [a] method for root cause analysis in a process engineering plant (Susteata, Abstract, teaches “provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like”; Susteata ¶ 0216 teaches an “adaptive modeling component can continually assess the impact of historical decisions and use this information to generate model structure or parameter changes, to establish causal relationships [(that is, root cause analysis)] that can exist in the model, to improve the stochastic measures assigned to outcomes, or to generate additional rules or heuristics for future economic analysis and decision making functions [(that is, a method for root cause analysis in a process engineering plant)]”), engineering information on the engineering plant, which contains information about the plant components and their interconnection in the engineering plant, being provided in digital form, the method comprising:
creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables (Susteata, Fig. 9, teaches collecting and analyzing machine data from an engineering plant [Examiner annotations in dashed-line text boxes]:
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Susteata ¶ 0016 teaches a “probabilistic determination model and analysis can be performed at various levels of data to factor the probabilistic effect of an event on various business concerns given various levels of uncertainty as well as the costs associated with an making an incorrect inference as to prognosing an event and its associated weight with respect to the overall business concern”; Susteata ¶ 0165 teaches “the setpoint 910, setup information, and/or one or more economic values 916 (e.g., related to or indicative of energy costs, which may vary with time, peak loading values, and current loading conditions, material viscosity values, and the like) are provided to the control system 908, as illustrated and described in greater detail hereinafter”; Susteata ¶ 0233 teaches “a production facility engineer can reconfigure a production facility (e.g., plant or factory floor) to ensure that end-product output is maximized from every aspect of production [(that is, the engineering plant)]”)Susteata ¶ 0028 teaches “[v]arious models including simulation models, rule-based system, expert system, or other modeling techniques may be used to establish the range of possible operating conditions and evaluate their potential for optimizing machinery operation”; Susteata ¶ 0063 teaches “the term ‘inference’ refers generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, a system or component state or condition, or can generate a probability distribution [(that is, probability distributions)] over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events and the combination of individual probabilities or certainties. For example, the probability of an observation can be combined with the probability associated with the validity of the applicable inference rule or rules [(that is, “inference rule” is an inference model)]”; Susteata ¶ 0093 teaches “data relating to a system/process can be collected and transmitted (e.g., via the Internet, wireless, satellite, optical fiber . . . ) to a remote prognostic engine that analyzes the data and makes inferences as to future state of the system (or subset thereof) based in part on the data. For example, a small facility in a rural location may operate numerous motors and pumps in a harsh environment not necessarily suitable for highly sensitive processing components. Accordingly, data can be gathered at such location, and transmitted in real-time (or discrete time) and analyzed at the remote location where the sensitive processing components reside along with databases (e.g., historical data, trend data, machine data, solutions data, diagnostic algorithms . . . [(that is, “historical data” is prior variables)]”) that can facilitate speedy analysis and diagnosis/prognosis of systems/machines/processes at the rural location”); and
entering a diagnosis mode of the inference model (Susteata ¶ 0063 teaches that “classifiers can be utilized in connection with performing a probabilistic or statistical based analysis / diagnosis / prognosis—Bayesian networks . . . can be utilized in accordance with the subject invention [(that is, entering a diagnosis mode of the inference model)]”) and
performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant (Susteata ¶ 0026 teaches “the invention provides for operating a motorized system, wherein a controller operatively associated with the system includes a diagnostic component to diagnose an operating condition associated with the pump. The operating conditions detected by the diagnostic component may include motor or pump faults, or failure and/or degradation, and/or failure prediction (e.g., prognostics) in one or more system components; also, Susteata ¶ 0084 teaches “[v]arious artificial intelligence schemes/techniques/systems (e.g., . . . Bayesian networks, naïve Bayesian networks, . . . ) can be employed in connection with making inferences regarding future states [(that is, Bayesian inference)] in accordance with the subject invention [(that is, performing a Bayesian inference of fault probabilities)]”;
[Examiner notes that a “Bayesian network” or “naïve Bayesian network” is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). This model is particularly useful for representing and solving problems that involve uncertainty and probabilistic events; to make “inferences” from a “Bayesian network” is a Bayesian inference];
Susteata ¶ 0140 teaches “the diagnostics system 132 may further comprise a preprocessing portion (not shown) operatively coupled to the neural network, which conditions the measured current prior to inputting the current into the neural network, as well as a post processing portion operatively coupled to the neural network to determine whether the change in condition signal is due to a fault condition related to a motorized system driving the pump 110 [(that is, utilizing measurement data from the engineering plant)]. In this regard, the post processing portion may comprise a fuzzy rule based expert system. In addition, the diagnostics system 132 may detect one or more faults relating to the operation of the pump 110 and/or one or more faults relating to the operation of a motor driving the pump 110 according to the measured current”).
Examiner notes that the Applicant’s preamble does not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
Regarding claim 18, Susteata teaches [a] system for root cause analysis in a process engineering plant (Susteata, Abstract, teaches “provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like”; Susteata ¶ 0216 teaches an “adaptive modeling component can continually assess the impact of historical decisions and use this information to generate model structure or parameter changes, to establish causal relationships [(that is, root cause analysis)] that can exist in the model, to improve the stochastic measures assigned to outcomes, or to generate additional rules or heuristics for future economic analysis and decision making functions [(that is, a method for root cause analysis in a process engineering plant)]”), the system comprising:
a processor and memory (Susteata ¶ 0179 teaches “the controller 966 may reside as instructions in the memory of the motor drive 960, which may be computed on an embedded processor circuit that controls the motor 906 in the motor drive 960 [(that is, a processor and a memory )]”);
a transformation module which creates an inference model which contains information about components of the engineering plant and interconnection of the components in the engineering plant as a probabilistic physical model of the engineering plant with probability distributions and prior variables from engineering information on the engineering plant (Susteata, Fig. 9, teaches collecting and analyzing machine data from an engineering plant [Examiner annotations in dashed-line text boxes]:
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Susteata ¶ 0016 teaches a “probabilistic determination model and analysis can be performed at various levels of data to factor the probabilistic effect of an event on various business concerns given various levels of uncertainty as well as the costs associated with an making an incorrect inference as to prognosing an event and its associated weight with respect to the overall business concern”; Susteata ¶ 0165 teaches “the setpoint 910, setup information, and/or one or more economic values 916 (e.g., related to or indicative of energy costs, which may vary with time, peak loading values, and current loading conditions, material viscosity values, and the like) are provided to the control system 908, as illustrated and described in greater detail hereinafter”; Susteata ¶ 0233 teaches “a production facility engineer can reconfigure a production facility (e.g., plant or factory floor) to ensure that end-product output is maximized from every aspect of production [(that is, from engineering information on the engineering plant)]”; Susteata ¶ 0063 teaches “the term ‘inference’ refers generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, a system or component state or condition, or can generate a probability distribution [(that is, probability distributions)] over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events and the combination of individual probabilities or certainties. For example, the probability of an observation can be combined with the probability associated with the validity of the applicable inference rule or rules [(that is, “inference rule” is an inference model)]”; Susteata ¶ 0093 teaches “data relating to a system/process can be collected and transmitted (e.g., via the Internet, wireless, satellite, optical fiber . . . ) to a remote prognostic engine that analyzes the data and makes inferences as to future state of the system (or subset thereof) based in part on the data. For example, a small facility in a rural location may operate numerous motors and pumps in a harsh environment not necessarily suitable for highly sensitive processing components. Accordingly, data can be gathered at such location, and transmitted in real-time (or discrete time) and analyzed at the remote location where the sensitive processing components reside along with databases (e.g., historical data, trend data, machine data, solutions data, diagnostic algorithms . . . [(that is, “historical data” is prior variables)]”) that can facilitate speedy analysis and diagnosis/prognosis of systems/machines/processes at the rural location”; Susteata ¶ 0182 teaches “correlation engine 910 correlates the information 900, 902, and/or 904 according to present operating conditions (e.g., as determined according to values from one or more of the process sensors 924, 938, 940, 941, 942, 944, 946, 948, and/or 954 [(that is, creates an inference model which contains information about components of the engineering plant and interconnection of the components in the engineering plant as a probabilistic physical model)]); and
an inference module which utilizes measurement data from the engineering plant to perform Bayesian inference of fault probabilities in a diagnosis mode of the inference model (Susteata ¶ 0026 teaches “the invention provides for operating a motorized system, wherein a controller operatively associated with the system includes a diagnostic component to diagnose an operating condition associated with the pump. The operating conditions detected by the diagnostic component may include motor or pump faults, or failure and/or degradation, and/or failure prediction (e.g., prognostics) in one or more system components; also, Susteata ¶ 0084 teaches “[v]arious artificial intelligence schemes/techniques/systems (e.g., . . . Bayesian networks, naïve Bayesian networks, . . . ) can be employed in connection with making inferences regarding future states [(that is, Bayesian inference)] in accordance with the subject invention [(that is, performing a Bayesian inference of fault probabilities)]”;
[Examiner notes that a “Bayesian network” or “naïve Bayesian network” is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). This model is particularly useful for representing and solving problems that involve uncertainty and probabilistic events; to make “inferences” from a “Bayesian network” is a Bayesian inference];
Susteata ¶ 0140 teaches “the diagnostics system 132 may further comprise a preprocessing portion (not shown) operatively coupled to the neural network, which conditions the measured current prior to inputting the current into the neural network, as well as a post processing portion operatively coupled to the neural network to determine whether the change in condition signal is due to a fault condition related to a motorized system driving the pump 110 [(that is, utilizing measurement data from the engineering plant)]. In this regard, the post processing portion may comprise a fuzzy rule based expert system. In addition, the diagnostics system 132 may detect one or more faults relating to the operation of the pump 110 and/or one or more faults relating to the operation of a motor driving the pump 110 according to the measured current”).
Examiner notes that the Applicant’s preamble does not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
Regarding claim 20, Susteata teaches [a] computer program product which is loaded into memory of a computer and which comprises software code sections with which the method according to claim 8 is executed when the computer program product is executed on the computer (Susteata ¶ 0179 teaches “the controller 966 may reside as instructions in the memory of the motor drive 960, which may be computed on an embedded processor circuit that controls the motor 906 in the motor drive 960 [(that is, a computer program product which is loaded into memory of a computer and which comprises software code sections with which the method according to claim 8 is executed when the computer program product is executed on the computer)]”).
As set out above regarding claim 8, Susteata teaches [a] method for root cause analysis in a process engineering plant (Susteata, Abstract, teaches “provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like”; Susteata ¶ 0216 teaches an “adaptive modeling component can continually assess the impact of historical decisions and use this information to generate model structure or parameter changes, to establish causal relationships [(that is, root cause analysis)] that can exist in the model, to improve the stochastic measures assigned to outcomes, or to generate additional rules or heuristics for future economic analysis and decision making functions [(that is, a method for root cause analysis in a process engineering plant)]”), engineering information on the engineering plant, which contains information about the plant components and their interconnection in the engineering plant, being provided in digital form, the method comprising:
creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables (Susteata, Fig. 9, teaches collecting and analyzing machine data from an engineering plant [Examiner annotations in dashed-line text boxes]:
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Susteata ¶ 0016 teaches a “probabilistic determination model and analysis can be performed at various levels of data to factor the probabilistic effect of an event on various business concerns given various levels of uncertainty as well as the costs associated with an making an incorrect inference as to prognosing an event and its associated weight with respect to the overall business concern”; Susteata ¶ 0165 teaches “the setpoint 910, setup information, and/or one or more economic values 916 (e.g., related to or indicative of energy costs, which may vary with time, peak loading values, and current loading conditions, material viscosity values, and the like) are provided to the control system 908, as illustrated and described in greater detail hereinafter”; Susteata ¶ 0233 teaches “a production facility engineer can reconfigure a production facility (e.g., plant or factory floor) to ensure that end-product output is maximized from every aspect of production [(that is, the engineering plant)]”)Susteata ¶ 0028 teaches “[v]arious models including simulation models, rule-based system, expert system, or other modeling techniques may be used to establish the range of possible operating conditions and evaluate their potential for optimizing machinery operation”; Susteata ¶ 0063 teaches “the term ‘inference’ refers generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, a system or component state or condition, or can generate a probability distribution [(that is, probability distributions)] over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events and the combination of individual probabilities or certainties. For example, the probability of an observation can be combined with the probability associated with the validity of the applicable inference rule or rules [(that is, “inference rule” is an inference model)]”; Susteata ¶ 0093 teaches “data relating to a system/process can be collected and transmitted (e.g., via the Internet, wireless, satellite, optical fiber . . . ) to a remote prognostic engine that analyzes the data and makes inferences as to future state of the system (or subset thereof) based in part on the data. For example, a small facility in a rural location may operate numerous motors and pumps in a harsh environment not necessarily suitable for highly sensitive processing components. Accordingly, data can be gathered at such location, and transmitted in real-time (or discrete time) and analyzed at the remote location where the sensitive processing components reside along with databases (e.g., historical data, trend data, machine data, solutions data, diagnostic algorithms . . . [(that is, “historical data” is prior variables)]”) that can facilitate speedy analysis and diagnosis/prognosis of systems/machines/processes at the rural location”); and
entering a diagnosis mode of the inference model (Susteata ¶ 0063 teaches that “classifiers can be utilized in connection with performing a probabilistic or statistical based analysis / diagnosis / prognosis—Bayesian networks . . . can be utilized in accordance with the subject invention [(that is, entering a diagnosis mode of the inference model)]”) and
performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant (Susteata ¶ 0026 teaches “the invention provides for operating a motorized system, wherein a controller operatively associated with the system includes a diagnostic component to diagnose an operating condition associated with the pump. The operating conditions detected by the diagnostic component may include motor or pump faults, or failure and/or degradation, and/or failure prediction (e.g., prognostics) in one or more system components; also, Susteata ¶ 0084 teaches “[v]arious artificial intelligence schemes/techniques/systems (e.g., . . . Bayesian networks, naïve Bayesian networks, . . . ) can be employed in connection with making inferences regarding future states [(that is, Bayesian inference)] in accordance with the subject invention [(that is, performing a Bayesian inference of fault probabilities)]”;
[Examiner notes that a “Bayesian network” or “naïve Bayesian network” is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). This model is particularly useful for representing and solving problems that involve uncertainty and probabilistic events; to make “inferences” from a “Bayesian network” is a Bayesian inference];
Susteata ¶ 0140 teaches “the diagnostics system 132 may further comprise a preprocessing portion (not shown) operatively coupled to the neural network, which conditions the measured current prior to inputting the current into the neural network, as well as a post processing portion operatively coupled to the neural network to determine whether the change in condition signal is due to a fault condition related to a motorized system driving the pump 110 [(that is, utilizing measurement data from the engineering plant)]. In this regard, the post processing portion may comprise a fuzzy rule based expert system. In addition, the diagnostics system 132 may detect one or more faults relating to the operation of the pump 110 and/or one or more faults relating to the operation of a motor driving the pump 110 according to the measured current”).
Examiner notes that the Applicant’s preamble does not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
Claim Rejections – 35 U.S.C. § 103
9. 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.
10. 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.
11. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
12. Claims 9 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of US Published Application 20180348717 to Zhao et al. [hereinafter Zhao ‘717].
Regarding claim 9, Susteata teaches all of the limitations of claim 8, as described above in detail.
wherein the Bayesian inference of the inference model is performed during a training mode of the inference model utilizing measurement data from the engineering plant (Susteata ¶ 0229 teaches “[b]y building rich intelligence into developed or created models, when these [inference] models are deployed they can automatically, dynamically, and continuously learn the production process being modeled [(that is, “learn” is the Bayesian inference of the inference model is performed during a training mode of the inference model)] and in so doing identify interdependencies or correlations to use in connection with future constraints that might arise in a production process”); and
* * *
Though Susteata teaches performing a Bayesian inference of fault probabilities, Susteata, however, does not explicitly teach –
* * *
wherein estimates of model parameters representing priors in the inference model are optimized.
But Zhao ‘717 teaches -
wherein estimates of model parameters representing priors in the inference model are optimized (Zhao ‘717 ¶ 0061 teaches “[t]he method 200 (step 216) may apply data screening and selection techniques to prepare and preprocess the loaded recent measurements [(that is, priors in the inference model)]. The method 200 (step 216) may also update model parameters partially or fully by using a recursive [projection latent structure (PLS) technique (algorithm)] [(that is, estimates of model parameters)], and re-calculate model statistics with the updated model parameters to track model health status. In some embodiments, the method 200 (step 216) may stop updating the model parameters in one or more of the following situations: (i) when model performance improves and remains at the improved level for a threshold, or (ii) input data (measurements) contain less moves over the recent data history [(that is, “stop updating model parameters . . . when model improves and remains” is estimates of model parameters . . . are optimized)]”).
Susteata and Zhao ‘717 are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Zhao ‘717 teaches building and training prediction inferential model using the datasets and updating the model parameters.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Susteata pertaining to learning a Bayesian inference of an inference model with the model parameter optimization of Zhao ‘717.
The motivation to do so is to “an improved approach to build, train, and deploy predictive inferential models for monitoring a plant process.” (Zhao ‘717 ¶ 0045).
Regarding claim 19, Susteata teaches all of the limitations of claim 18, as described above in detail.
wherein the inference module is further configured to perform Bayesian inference of the inference model (Susteata ¶ 0229 teaches “[b]y building rich intelligence into developed or created models, when these [inference] models are deployed they can automatically, dynamically, and continuously learn the production process being modeled [(that is, “learn” is to perform Bayesian inference of the inference model)] and in so doing identify interdependencies or correlations to use in connection with future constraints that might arise in a production process”); and
* * *
Though Susteata teaches performing a Bayesian inference of fault probabilities, Susteata, however, does not explicitly teach –
* * *
and wherein estimates of model parameters representing priors in the inference model are optimized.
But Zhao ‘717 teaches –
* * *
and wherein estimates of model parameters representing priors in the inference model are optimized (Zhao ‘717 ¶ 0061 teaches “[t]he method 200 (step 216) may apply data screening and selection techniques to prepare and preprocess the loaded recent measurements [(that is, priors in the inference model)]. The method 200 (step 216) may also update model parameters partially or fully by using a recursive [projection latent structure (PLS) technique (algorithm)] [(that is, estimates of model parameters)], and re-calculate model statistics with the updated model parameters to track model health status. In some embodiments, the method 200 (step 216) may stop updating the model parameters in one or more of the following situations: (i) when model performance improves and remains at the improved level for a threshold, or (ii) input data (measurements) contain less moves over the recent data history [(that is, “stop updating model parameters . . . when model improves and remains” is estimates of model parameters . . . are optimized)]”).
Susteata and Zhao ‘717 are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Zhao ‘717 teaches building and training prediction inferential model using the datasets and updating the model parameters.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Susteata pertaining to learning a Bayesian inference of an inference model with the model parameter optimization of Zhao ‘717.
The motivation to do so is to “an improved approach to build, train, and deploy predictive inferential models for monitoring a plant process.” (Zhao ‘717 ¶ 0045).
13. Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of Lo et al., "Bond Graph based Bayesian Network for Fault Diagnosis," Applied Soft Computing (2011) [hereinafter Lo].
Regarding claim 10, Susteata teaches all of the limitations of claim 8, as described above in detail.
Though Susteata teaches performing a Bayesian inference of fault probabilities, Susteata, however, does not explicitly teach –
wherein the inference model is created using a bond graph from the engineering information.
But Lo teaches -
wherein the inference model (Lo, left column of p. 1209, “2. Bayesian Network,” third paragraph, teaches “Inference in the Bayesian network is the task of computing the probability of each variable when other variables’ values are known [(that is, the inference model)]. That means once some evidence about variables’ states are asserted into the network, the effect of evidences will be propagated through the network and in every propagation the probabilities of adjacent nodes are updated”) is created using a bond graph from the engineering information (Lo, left column of p. 1209, “1. Introduction,” first partial paragraph, teaches “[t]he task of identifying system variables [(that is, the engineering information)] to construct Bayesian network is completed and the localization of faulty components from Bayesian network is enhanced since they are already represented in the bond graph model [(that is, the inference model is created using a bond graph from the engineering information)]”).
Susteata and Lo are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Lo teaches localization of faulty components from a Bayesian network is enhanced since they are represented in the bond graph model.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Susteata pertaining to learning a Bayesian inference of an inference model with the bond graph model enhancement of Lo.
The motivation to do so is because the “performance of the proposed fault diagnosis scheme based on bond graph derived Bayesian network is demonstrated through simulation studies.” (Lo, Abstract).
14. Claim 11 is rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of US Published Application 20180348717 to Zhao et al. [hereinafter Zhao ‘717], and Lo et al., "Bond Graph based Bayesian Network for Fault Diagnosis," Applied Soft Computing (2011) [hereinafter Lo].
Regarding claim 11, the combination of Susteata and Zhao ‘717 teaches all of the limitations of claim 9, as described above in detail.
Though Susteata and Zhao ‘717 teaches performing a Bayesian inference of fault probabilities, the combination of Susteata and Zhao ‘717, however, does not explicitly teach –
wherein the inference model is created using a bond graph from the engineering information.
But Lo teaches -
wherein the inference model (Lo, left column of p. 1209, “2. Bayesian Network,” third paragraph, teaches “Inference in the Bayesian network is the task of computing the probability of each variable when other variables’ values are known [(that is, the inference model)]. That means once some evidence about variables’ states are asserted into the network, the effect of evidences will be propagated through the network and in every propagation the probabilities of adjacent nodes are updated”) is created using a bond graph from the engineering information (Lo, left column of p. 1209, “1. Introduction,” first partial paragraph, teaches “[t]he task of identifying system variables [(that is, the engineering information)] to construct Bayesian network is completed and the localization of faulty components from Bayesian network is enhanced since they are already represented in the bond graph model [(that is, the inference model is created using a bond graph from the engineering information)]”).
Susteata and Lo are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Zhao ‘717 teaches building and training prediction inferential model using the datasets and updating the model parameters. Lo teaches localization of faulty components from a Bayesian network is enhanced since they are represented in the bond graph model.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Susteata and Zhao ‘717 pertaining to learning a Bayesian inference of an inference model having model parameter optimization with the bond graph model enhancement of Lo.
The motivation to do so is because the “performance of the proposed fault diagnosis scheme based on bond graph derived Bayesian network is demonstrated through simulation studies.” (Lo, Abstract).
15. Claim 12 and 15 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of Lechevalier et al., “Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing,” IEEE (2014) [hereinafter Lechevalier].
Regarding claims 12, Susteata teaches all of the limitations of claim 8, as described above in detail.
Though Susteata teaches performing a Bayesian inference of fault probabilities, Susteata, however, does not explicitly teach –
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library which contains code to be generated and inference variables for each component type in the engineering plant for which Bayesian inference is to be performed; and
wherein the inference model of the engineering plant is created from the metamodel.
But Lechevalier teaches -
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library (Lechevalier, Fig. 1, teaches a metamodel framework for an engineering plant [Examiner annotation in dashed-line text boxes}:
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Lechevalier, left column of p. 991, “Domain-Specific Modeling Environment,” first partial paragraph, teaches a “[domain specific language (DSL)] should allow users to instantiate concepts from the meta-models or reuse existing models from existing libraries [(that is, adopting templates from a model library)]. From this system description, it must be possible to automatically generate the predictive models and execute algorithms necessary to make predictions [(that is, a metamodel)]”) which contains code to be generated and inference variables for each component type in the engineering plant (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module,” first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the [domain-specific modeling environment (DSME)]. The diagnostic and predictive model to be defined depends on the variables that the user wants to observe and predict [(that is, inference variables for each component type in the engineering plant)], and the characteristics of the systems being defined. Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification. . . . In addition, data are needed to make the model accurate through training and validation. There must be a communication between the diagnostic and predictive model”; Lechevalier, Fig. 4, teaches a library of machine tools [Examiner annotations in dashed-line text boxes]:
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Lechevalier, right column of p. 993, “IV. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “[l]ibraries that partially instantiate the meta-concepts can be defined for the DSME. These libraries would define commonly used objects in the domain. Figure 4 shows an example of a machine library [(that is, each component type in the engineering plant)]”) for which Bayesian inference is to be performed (Lechevalier, right column of p. 989, “D. Application in Predictive Maintenance of Manufacturing Equipment,” first paragraph, teaches “[e]nterprises can take advantage of predictive modeling to plan for maintenance and achieve significant cost savings during maintenance. In [20], the authors define a Bayesian network to predict machine maintenance needs. The equipment has two condition monitoring values called CM1 and CM2. . . . The authors define a Bayesian network to model the machine condition and the transition probabilities of the condition modification [(that is, for which Bayesian inference is to be performed)]”); and
wherein the inference model of the engineering plant is created from the metamodel (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module, first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the DSME. . . . Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification [(that is, wherein the inference model of the engineering plant is created from the metamodel)]”).
Susteata and Lechevalier are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Lechevalier teaches a metamodel for creating a domain-specific model for a production system.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Susteata pertaining to learning a Bayesian inference of an inference model having model parameter optimization with the domain-specific metamodel of Lechevalier.
The motivation to do so is because “ [t]he goal of this framework is to make it easier for manufacturing domain experts to build models and run diagnostic and predictive analytics. With the framework, these tasks should not require extensive knowledge of machine learning techniques.” (Lechevalier, right column at p. 990, “IV. Predictive Analytics Framework for Manufacturing,” second paragraph).
Regarding claim 15, the combination of Susteata and Lechevalier teaches all of the limitations of claim 12, as described above in detail.
Lechevalier teaches -
wherein the engineering information on the engineering plant comprises a piping and instrumentation flow diagram (P&I) flow diagram (Lechevalier, Figure 2, teaches a factory production chain [Examiner annotations in dashed-line text boxes]:
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Lechevalier, left column of p. 992, “VI. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “Let’s suppose that a manufacturer wants to model a factory production chain as shown in Figure 2 [(that is, engineering information on the engineering plant)]. The production chain consists of a series of machines. Parts and raw materials come in from three different flows (starting from the green points on the left) and merge into a fastening machine. In this production chain, parts from the die casting machine are distributed into three different turning machines running in parallel (the machines may have different performance parameters, and therefore different energy use and throughput). A possible requirement for the manufacturer would be to optimize energy or time by controlling how the parts are distributed among three turning machines [(that is, a piping and instrumentation flow diagram (P&I) flow diagram)]”).
16. Claims 13 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of US Published Application 20180348717 to Zhao et al. [hereinafter Zhao ‘717], and Lechevalier et al., “Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing,” IEEE (2014) [hereinafter Lechevalier].
Regarding claim 13, the combination of Susteata and Zhao ‘717 teach all of the limitations of claim 9, as described above in detail.
Though Susteata and Zhao ‘717 teach performing a Bayesian inference of fault probabilities, the combination of Susteata and Zhao ‘717, however, does not explicitly teach –
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library which contains code to be generated and inference variables for each component type in the engineering plant for which Bayesian inference is to be performed; and
wherein the inference model of the engineering plant is created from the metamodel.
But Lechevalier teaches -
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library (Lechevalier, Fig. 1, teaches a metamodel framework for an engineering plant [Examiner annotation in dashed-line text boxes}:
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Lechevalier, left column of p. 991, “Domain-Specific Modeling Environment,” first partial paragraph, teaches a “[domain specific language (DSL)] should allow users to instantiate concepts from the meta-models or reuse existing models from existing libraries [(that is, adopting templates from a model library)]. From this system description, it must be possible to automatically generate the predictive models and execute algorithms necessary to make predictions [(that is, a metamodel)]”) which contains code to be generated and inference variables for each component type in the engineering plant (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module,” first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the [domain-specific modeling environment (DSME)]. The diagnostic and predictive model to be defined depends on the variables that the user wants to observe and predict [(that is, inference variables for each component type in the engineering plant)], and the characteristics of the systems being defined. Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification. . . . In addition, data are needed to make the model accurate through training and validation. There must be a communication between the diagnostic and predictive model”; Lechevalier, Fig. 4, teaches a library of machine tools [Examiner annotations in dashed-line text boxes]:
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Lechevalier, right column of p. 993, “IV. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “[l]ibraries that partially instantiate the meta-concepts can be defined for the DSME. These libraries would define commonly used objects in the domain. Figure 4 shows an example of a machine library [(that is, each component type in the engineering plant)]”) for which Bayesian inference is to be performed (Lechevalier, right column of p. 989, “D. Application in Predictive Maintenance of Manufacturing Equipment,” first paragraph, teaches “[e]nterprises can take advantage of predictive modeling to plan for maintenance and achieve significant cost savings during maintenance. In [20], the authors define a Bayesian network to predict machine maintenance needs. The equipment has two condition monitoring values called CM1 and CM2. . . . The authors define a Bayesian network to model the machine condition and the transition probabilities of the condition modification [(that is, for which Bayesian inference is to be performed)]”); and
wherein the inference model of the engineering plant is created from the metamodel (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module, first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the DSME. . . . Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification [(that is, wherein the inference model of the engineering plant is created from the metamodel)]”).
Susteata, Zhao ‘717, and Lechevalier are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Zhao ‘717 teaches building and training prediction inferential model using the datasets and updating the model parameters. Lechevalier teaches a metamodel for creating a domain-specific model for a production system.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Susteata and Zhao ‘717 pertaining to learning a Bayesian inference of an inference model having model parameter optimization with the domain-specific metamodel of Lechevalier.
The motivation to do so is because “ [t]he goal of this framework is to make it easier for manufacturing domain experts to build models and run diagnostic and predictive analytics. With the framework, these tasks should not require extensive knowledge of machine learning techniques.” (Lechevalier, right column at p. 990, “IV. Predictive Analytics Framework for Manufacturing,” second paragraph).
Regarding claim 16, the combination of Susteata, Zhao ‘717, and Lechevalier teaches all of the limitations of claim 13, as described above in detail.
Lechevalier teaches -
wherein the engineering information on the engineering plant comprises a piping and instrumentation flow diagram (P&I) flow diagram (Lechevalier, Figure 2, teaches a factory production chain [Examiner annotations in dashed-line text boxes]:
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Lechevalier, left column of p. 992, “VI. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “Let’s suppose that a manufacturer wants to model a factory production chain as shown in Figure 2 [(that is, engineering information on the engineering plant)]. The production chain consists of a series of machines. Parts and raw materials come in from three different flows (starting from the green points on the left) and merge into a fastening machine. In this production chain, parts from the die casting machine are distributed into three different turning machines running in parallel (the machines may have different performance parameters, and therefore different energy use and throughput). A possible requirement for the manufacturer would be to optimize energy or time by controlling how the parts are distributed among three turning machines [(that is, a piping and instrumentation flow diagram (P&I) flow diagram)]”).
17. Claims 14 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20090204234 to Susteata et al. [hereinafter Susteata] in view of Lo et al., "Bond Graph based Bayesian Network for Fault Diagnosis," Applied Soft Computing (2011) [hereinafter Lo], and Lechevalier et al., “Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing,” IEEE (2014) [hereinafter Lechevalier].
Regarding claim 14, the combination of Susteata and Lo teaches all of the limitations of claim 10, as described above in detail.
Though Susteata and Lo teach performing a Bayesian inference of fault probabilities based on a bond graph, the combination of Susteata and Lo, however, does not explicitly teach –
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library which contains code to be generated and inference variables for each component type in the engineering plant for which Bayesian inference is to be performed; and
wherein the inference model of the engineering plant is created from the metamodel.
But Lechevalier teaches -
wherein a metamodel of the engineering plant is initially generated from the engineering information on the engineering plant by adopting templates from a model library (Lechevalier, Fig. 1, teaches a metamodel framework for an engineering plant [Examiner annotation in dashed-line text boxes}:
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Lechevalier, left column of p. 991, “Domain-Specific Modeling Environment,” first partial paragraph, teaches a “[domain specific language (DSL)] should allow users to instantiate concepts from the meta-models or reuse existing models from existing libraries [(that is, adopting templates from a model library)]. From this system description, it must be possible to automatically generate the predictive models and execute algorithms necessary to make predictions [(that is, a metamodel)]”) which contains code to be generated and inference variables for each component type in the engineering plant (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module,” first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the [domain-specific modeling environment (DSME)]. The diagnostic and predictive model to be defined depends on the variables that the user wants to observe and predict [(that is, inference variables for each component type in the engineering plant)], and the characteristics of the systems being defined. Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification. . . . In addition, data are needed to make the model accurate through training and validation. There must be a communication between the diagnostic and predictive model”; Lechevalier, Fig. 4, teaches a library of machine tools [Examiner annotations in dashed-line text boxes]:
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Lechevalier, right column of p. 993, “IV. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “[l]ibraries that partially instantiate the meta-concepts can be defined for the DSME. These libraries would define commonly used objects in the domain. Figure 4 shows an example of a machine library [(that is, each component type in the engineering plant)]”) for which Bayesian inference is to be performed (Lechevalier, right column of p. 989, “D. Application in Predictive Maintenance of Manufacturing Equipment,” first paragraph, teaches “[e]nterprises can take advantage of predictive modeling to plan for maintenance and achieve significant cost savings during maintenance. In [20], the authors define a Bayesian network to predict machine maintenance needs. The equipment has two condition monitoring values called CM1 and CM2. . . . The authors define a Bayesian network to model the machine condition and the transition probabilities of the condition modification [(that is, for which Bayesian inference is to be performed)]”); and
wherein the inference model of the engineering plant is created from the metamodel (Lechevalier, left column of p. 991, “D. Diagnostic and Predictive Model Generation Module, first paragraph, teaches “[t]he most important functionality provided by this framework is the automatic generation of a predictive analytics model from the system specification created using the DSME. . . . Manufacturers should be able to define their objectives by using abstractions of the meta-model. The meta-model must also provide abstractions for analytical models to support automatic generation of the predictive model from the system specification [(that is, wherein the inference model of the engineering plant is created from the metamodel)]”).
Susteata, Lo, and Lechevalier are from the same of similar field of endeavor. Susteata teaches the reasoning about or inferring states of a system, environment, and/or user from a set of observations as captured via events and/or data through inference and Bayesian modeling. Lo teaches localization of faulty components from a Bayesian network is enhanced since they are represented in the bond graph model. Lechevalier teaches a metamodel for creating a domain-specific model for a production system.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Susteata and Lo pertaining to learning a Bayesian inference of an inference model having model parameter optimization based on a bond graph with the domain-specific metamodel of Lechevalier.
The motivation to do so is because “ [t]he goal of this framework is to make it easier for manufacturing domain experts to build models and run diagnostic and predictive analytics. With the framework, these tasks should not require extensive knowledge of machine learning techniques.” (Lechevalier, right column at p. 990, “IV. Predictive Analytics Framework for Manufacturing,” second paragraph).
Regarding claim 17, the combination of Susteata, Lo, and Lechevalier teaches all of the limitations of claim 14, as described above in detail.
Lechevalier teaches -
wherein the engineering information on the engineering plant comprises a piping and instrumentation flow diagram (P&I) flow diagram (Lechevalier, Figure 2, teaches a factory production chain [Examiner annotations in dashed-line text boxes]:
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Lechevalier, left column of p. 992, “VI. Example: Domain-Specific Model for a Production System,” first paragraph, teaches “Let’s suppose that a manufacturer wants to model a factory production chain as shown in Figure 2 [(that is, engineering information on the engineering plant)]. The production chain consists of a series of machines. Parts and raw materials come in from three different flows (starting from the green points on the left) and merge into a fastening machine. In this production chain, parts from the die casting machine are distributed into three different turning machines running in parallel (the machines may have different performance parameters, and therefore different energy use and throughput). A possible requirement for the manufacturer would be to optimize energy or time by controlling how the parts are distributed among three turning machines [(that is, a piping and instrumentation flow diagram (P&I) flow diagram)]”).
Response to Arguments
18. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Claim Rejections – 35 U.S.C. § 101
19. Under Step 2A Prong Two, Applicant submits that “[i]n determining whether applicants' claims include subject matter that amounts to significantly more than a mere abstract idea, the following should be noted.” (Response at p. 7 (citing MPEP § 2106.04(d)(1))).
In this regard, Applicant submits that “[a]s stated, applicant's claimed method and system provide an improvement to the technological field of process engineering plants (automation systems), and is therefore in fact limited to a useful practical application.” (Response at p. 9).
With regard to an improvement, Applicant argues the “clamed invention is directed to a system and method for combining model-based and signal-based methods in a hybrid approach to maximize their advantages and reduce their disadvantages, which is identified as a problem in the technological field of the invention (see, e.g., paragraphs [0003] to [0015] of the published application).” (Response at pp. 09-10 (quoting Specification at page 22, line 15 through page 23, line 47)).
With respect to the claims, “applicants contend the claims include wording to the effect of
"creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables",
[(claim 8, lines 5-7)] i.e., creation of an inference model forming a probabilistic physical model of the plant, which reflects actual physical relationships rather than abstract math, and
"entering a diagnosis mode of the inference model and performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant",
[(claim 8, lines 8-10)] i.e., entering a diagnosis mode utilizing real-time measurement data from the plant, applying Bayesian inference to improve operational reliability, which provides an improvement to the technological field of process engineering plants (automation systems), where then plant can in particular be a process engineering or chemical engineering plant operating in the fields of the chemical industry, the food and beverage industry, environmental technology industry, the pharmaceutical industry or the gas and oil industry (See, e.g., paragraph [0003] of the published application).” (Response at p. 11).
Relying on McRO, Applicant submits that the “claims in fact encompass ‘a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome’, which is consistent with the holding of McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. By enabling faster and more accurate fault diagnosis, applicants' claimed method and system reduce downtime and enhance safety. These improvements are rooted in the industrial context and cannot be performed in the human mind or on paper, which is a clear improvement that is consistent with the holding of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).” (Response at p. 12).
Applicant submits that “[i]n sum, applicants' claimed invention is not merely an abstract idea, but rather a specific technological process applied to a specific industrial environment, i.e., a process engineering plant.” (Response at p. 12).
Examiner’s Response:
The Applicant’s arguments are unpersuasive because the claims recite a mental process under Step 2A Prong One, and also, the claims do not recite additional elements that serve to integrate the abstract idea into a practical application when considered as a whole under Step 2A Prong Two.
Exemplar claim 8 recites:
8. (Previously Presented) A method for root cause analysis in a process engineering plant, engineering information on the engineering plant, which contains information about the plant components and their interconnection in the engineering plant, being provided in digital form, the method comprising:
[(a)] creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables; and
[(b)] entering a diagnosis mode of the inference model and
[(b.1)] performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant.
(claim 8 (emphasis added by Examiner)). To the extent Applicant relies upon terms contained within the preamble, this language is not positively recited within the claim. The preamble recites:
A method for root cause analysis in a process engineering plant, engineering information on the engineering plant, which contains information about the plant components and their interconnection in the engineering plant, being provided in digital form, the method comprising:
* * *
For example, the terms “plant components” and “root cause analysis” are only recited in the claim preambles. (see claims 1).
Applicant’s preambles do not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
The body of claims 1 and 18 are directed to modeling an engineering plant.” (claim 1, lines 1-4; see also claim 18, lines 1-2).
“Under Step 2A, Prong Two, USPTO personnel must assess whether the claim as a whole integrates the judicial exception into a practical application of the exception.” (MPEP § 2106.04 sub II.A.2). Also, “[a]s explained in MPEP 2106.04(d), subsection III, the Step 2A, ‘Prong Two analysis considers the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application.’” (2024 SME Guidance at p. 58136, 89 Fed. Reg. 137 (17 July 2024)).
As set out above in detail, the additional elements recited in the claim beyond the identified judicial exception include “an inference model” and “a probabilistic physical model,” which are each recited at a high-level of generality, and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The additional elements of “[(a)] creating an inference model” and “[(b)] entering a diagnosis mode of the model” is using the generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the elements of a “processing engineering plant” and “plant components and their interconnection” is merely linking the abstract idea to a field of use (that is, forming inferences directed to a processing plant) that does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(h)).
Though Applicant submits that these additional elements, when considering the claim as a whole, integrates the abstract idea into a practical application; however, the claims are directed generally to modeling “a processing engineering plant,” which is merely claiming the idea of a solution or outcome. (cf. 2024 SME Guidance at p. 58137, 89 Fed. Reg. 137 (17 July 2024)). Moreover, “[a] claim that integrates a judicial exception into a practical application of the exception will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize or preempt the judicial exception.” (2024 SME Guidance at p. 58136, 89 Fed. Reg. 137 (17 July 2024)). The instant claims, however, do not impose a meaningful limit on the judicial exception.
Accordingly, Examiner respectfully submits that the pending claims are subject-matter ineligible for the reasons set out hereinabove.
20. With regards to the claimed “inference model” and “Bayesian inference,” under Step 2A Prong Two, Applicant argues “these concepts are integrated into a specific application, i.e., root cause analysis in a process engineering plant [(claim 8, line 1)].
In particular, independent claim 8 requires engineering information about plant components and their interconnections in digital form, representing a real-world physical system. That is, creation of an inference model forming a probabilistic physical model of the plant, which reflects actual physical relationships rather than abstract math, and a diagnosis mode utilizing real-time measurement data from the plant, applying Bayesian inference to improve operational reliability. The foregoing is more than a mere generic mathematical algorithm; it is a technical solution to a technical problem identifying fault causes in complex industrial installations.” (Response at p. 12 (emphasis added by Examiner)).
“Moreover, applicants' claimed method improves the functioning of process engineering plants by enabling faster and more accurate fault diagnosis, which reduces downtime and enhances safety. These improvements are rooted in the industrial context and cannot be performed in the human mind or on paper. Applicants claimed invention thus provides significantly more than the alleged abstract idea, consistent with DDR Holdings and McRO.
Lastly, independent claim 8 applies Bayesian inference in a manner that is specifically tailored to the structure and operation of process engineering plants, which constitutes a practical application under the USPTO's 2019 ‘Revised Patent Subject Matter Eligibility Guidance’ and the USPTO's 2025 ‘Reminders on Evaluating Subject Matter Eligibility of Claims.’” (Response at p. 13).
Examiner’s Response:
The Applicant’s argument is unpersuasive because the claims do not recite the additional elements, considering the claim as a whole, will apply, rely on, or use the abstract idea in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize or preempt the judicial exception. In this manner, the additional elements do not serve to integrate the abstract idea into a practical application.
Applicant submits that the claims apply a “Bayesian inference in a manner that is specifically tailored to the structure and operation of processing engineering plants.” (Response at p. 13). However, the claims recite simply “which contains information about the plant components and their interconnection,” which does not appear to be in the nature of “tailored specificity.”
Also, with respect to the argument that the claimed method “improves the functioning of process engineering plants by enabling faster and more accurate fault diagnosis, which reduces downtime and enhances safety.” (Response at p. 13). But the claimed methods are not rendered patent eligible by the fact that by using existing machine learning technology they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. That is, in the context of computer-assisted methods, such claims are not made patent eligible under § 101 simply because they speed up an otherwise human activity.
Accordingly, the instant claims, considered as a whole, recite the use of generic computer components to implement the abstract idea, and does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). Further, the activities of “[(a)] creating an inference model” and “[(b)] entering a diagnosis mode of the model” is using generic computer components (inference model, probabilistic physical model) to implement the abstract idea, (MPEP § 2106.05(f)).
Accordingly, the pending claims are subject-matter ineligible, as set out above in detail.
Claim Rejections – 35 U.S.C. §§ 102 & 103
21. Regarding the Susteata prior art, “[r]espectfully, applicants believe this position is not well taken [because] [i]n short, ‘Bayesian probabilistic methods’ (although related) are not a ‘Bayesian inference’” of Applicant’s claims. (Response at p. 15 (quoting Office action at pp. 8-9 (“Susteata teaches . . . performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant”)).
Applicant argues “The skilled person knows Bayesian inference is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. Fundamentally, Bayesian inference uses a prior distribution to estimate posterior probabilities. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data.” (Response at p. 16 (citing to Wikipedia (“Bayesian inference”)).
“In contrast, Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as the reasonable expectation representing a state of knowledge or as the quantification of a personal belief.
Bayesian interpretation of probability can be viewed as an extension of propositional logic that enables reasoning with hypotheses; that is, with propositions whose truth or falsity is unknown. In the Bayesian view, a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability.” (Response at p. 16 (citing to Wikipedia (“Bayesian Probability”)).
Examiner’s Response:
Examiner respectfully submits that the Susteata reference teaches the limitation of “a Bayesian inference” as set out by the claims.
Exemplar claim 8 recites:
8. (Previously Presented) A method for root cause analysis in a process engineering plant, engineering information on the engineering plant, which contains information about the plant components and their interconnection in the engineering plant, being provided in digital form, the method comprising:
creating an inference model from the engineering information to form a probabilistic physical model of the engineering plant with probability distributions and prior variables; and
entering a diagnosis mode of the inference model and
performing a Bayesian inference of fault probabilities utilizing measurement data from the engineering plant.
Regarding the Susteata prior art, “applicants believe this position is not well taken [because] [i]n short, ‘Bayesian probabilistic methods’ (although related) are not a ‘Bayesian inference’.” (Response at p. 15).
Susteata, however, teaches “[v]arious artificial intelligence schemes/techniques/systems ( e.g., . . . Bayesian networks, naïve Bayesian networks, . . . ) can be employed in connection with making inferences regarding future states in accordance with the subject invention [(that is, using “Bayesian networks, naïve Bayesian networks” for “making inferences” is performing a Bayesian inference )]”. (Susteata ¶ 0084).
Accordingly, Susteata anticipates claims 8, 18, and 20 because each and every element as set forth in the claims is found, either expressly or inherently described, in the prior art reference of Susteata. (MPEP § 2131).
Moreover, the rejections hereinabove clearly sets forth which claim limitations are taught by each of the prior art references, and the reason why it would be obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant's invention to combine their teachings, and Applicant has not explained why the cited prior art references cannot be combined in the manner set forth in the rejection.
Conclusion
22. 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.
21. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US Published Application 20210110089 to Chen et al.) teaches a likelihood-free Bayesian inference is applied to estimating the parameters of a robotics simulator. In an embodiment the Bayesian inferencing techniques described herein provide a full distribution, therefore quantifying the uncertainty of the simulator with respect to reality.
(US Published Application 20050043922 to Weidl et al.) teaches the creation of the initial Bayesian network graphs can be done automatically i.e. without intervention by the user. This saves development time. Use of data that already exist in a hierarchically organised data structure may also reduce significantly the engineering efforts on transferring the collected domain knowledge and operator experience that is obtained e.g. through interviews on the plant into Bayesian network compatible graphs.
(Jun et al., “A Bayesian network-based approach for fault analysis,” Expert Systems with Applications (2017)) teaches for high-value assets such as certain types of plant equipment, the total amount of resources devoted to Operation and Maintenance may substantially exceed the resources expended in acquisition and installation of the asset, because high-value assets have long useful lifetimes. Effective condition-based maintenance requires an effective fault analysis method based on gathered sensor data. In this vein, this paper proposes a Bayesian network-based fault analysis method, from which novel fault identification, inference, and sensitivity analysis methods are developed.
22. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 Examiner notes that the limitations references are provided for the limited purpose of aiding in the subject matter eligibility evaluation.