DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-2 are amended. Claims 1-6 are pending.
Response to Arguments
Applicant's arguments filed 12/31/2025 have been fully considered.
Regarding the objection to claim 1, and as noted by Applicant on page 9 of the response, the amendment to claim 1 overcomes the objection, which is withdrawn.
Regarding the rejections of claims 1-6 under 101, the Examiner respectfully disagrees with Applicant’s arguments that amended claims 1 and 2 overcome the rejections for the following reasons.
On page 9 of the response, Applicant contends that amended claims 1 and 2 as a whole integrate the judicial exception into a practical application. In support, on page 10 of the response, Applicant asserts that claim 1 as a whole is an automated, data-driven decision-making and execution process having the purpose of proactively preventing equipment failure, optimizing maintenance plans, and reducing unplanned downtime. Applicant further asserts that the limitations added by amendment, “generating, by the equipment maintenance decision-making platform, a maintenance decision based on both the failure mode and the residual life predicted value with its confidence interval; and displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff,” is a direct, automated application of the predictive results that transforms the system from merely an “analysis tool” into a decision-making control system, which constitutes a practical application.
The Examiner submits, as explained in further detail in the grounds of rejection under 101, that the step of “generating, by the equipment maintenance decision-making platform, a maintenance decision based on both the failure mode and the residual life predicted value with its confidence interval” itself falls within the mental processes judicial exception. The related step “displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff,” while not falling within a judicial exception, represents conventional computer processing activity (outputting processing results via display) having no particularized functional relation to the steps falling within the judicial exception (e.g., manner of display characterized with respect to particular intermediary or final processing steps). Therefore, the displaying step appears to constitute insignificant extra solution (post solution) activity that neither integrates the steps falling within the judicial exception into a particular practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
On pages 10-11 of the response, Applicant contends that steps 31-34 are not related to an abstract idea. In support on page 11 of the response, Applicant asserts that these steps are unconventional and constitute specific technical means designed to solve the technical problem of “achieving high-reliability coupling prediction in complex device networks.”
The Examiner submits that the cited steps (listed on pages 10-11), except for “extracting …” are related to an abstract idea because, as set forth in the grounds of rejection, they are found to fall within the mathematical concepts judicial exception. The Examiner submits that while the recited sequence of steps appear to be unconventional and furthermore may result in a useful processing result, this is not sufficient for establishing that the combined claim elements integrate the judicial exception into a practical application (e.g., improved functioning of a computer or technical improvement to a technical field) or result in the claim as a whole amounting to significantly more than the judicial exception.
On page 11 of the response, Applicant contends that claim 1 itself amounts to significantly more than the judicial exception. In support, Applicant asserts that “something more” may be understood as a combination of creative technical steps, and notes that claim 1 has been found patentably distinguishable over the prior arts. Applicant’s arguments on this point are generalized, citing “the hardware, the specific temporal sample, the probabilistic graphical model and the specific formulas in claim 1 that jointly form the equipment failure mode predetermination and residual life prediction coupling system that is able to determine both the equipment failure mode and the residual life of the equipment.”
The Examiner notes that the combined features in claim 1 (and similarly claim 2) appear to be patentably distinct over the prior arts. However, the particular sub-combination of elements that is found patentably distinct includes elements falling within the judicial exception, and the claim includes no individual or combination of additional elements that functionally relate to the judicial exception in a manner that results in the judicial exception being integrated into a practical application and/or the claim as a whole amounting to significantly more than the judicial exception.
On page 11 of the response, Applicant further contends that the improvement to the accuracy of the maintenance operations and the efficiency of resource utilization constitutes an improvement on the prior art.
The Examiner submits that accuracy of maintenance decision making is embodied only as an assumed result of the recited processing steps that, as explained in the grounds of rejection, do not themselves represent an improvement in processing functionality. The Examiner submits that claim 1 is directed to an abstract idea because the claim does not appear to include any combination of elements that results in the judicial exception being integrated into a practical application. For example, considering the factors set forth in MPEP 2106.05(b), the technology recited in claim 1 is characterized very generally such that not even a specific “field-of-use” is evident (e.g., a particular technical industry itself and/or particular types/configurations of particular specialized sensors, particular types of data collected/processed, particular equipment types/configurations, etc.). Instead, the functional steps are implemented by a generic computer processing system as a tool to implement the mathematical and/or mental process steps (see MPEP 2106.05(f)), such that the additional elements are insufficient in combination with the math/mental steps to prevent monopolization of the mathematical and/or mental process functions over a very wide array of possible applications.
For the foregoing reasons, the rejections of claims 1 and 2 under 101 are maintained.
Claim Objections
Claim 1 is objected to because of the following informalities:
For claim 1, “decision” is misspelled as “decison” in the element beginning with “generating, by the equipment maintenance decision-making platform, a maintenance decison.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 1, substantially representative also of independent claim 2, recites:
“An equipment failure mode predetermination and residual life prediction coupling system comprising:
an equipment maintenance network and an equipment maintenance decision-making platform which are in communication connection with each other, wherein the equipment maintenance network is configured to determine a failure mode and predict a residual life, and the equipment maintenance decision-making platform is configured to receive the failure mode and residual life of equipment and transmit the failure mode and residual life of the equipment to operation and maintenance staff,
the equipment maintenance network comprises equipment nodes, sensor sets, abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module;
each of the sensor sets comprises a plurality of sensors configured to acquire equipment health information of the corresponding equipment node;
each of the abnormal state recognition modules is configured to receive and cache the equipment health information and recognize a time of occurrence of an exception of equipment to be detected;
the equipment failure mode predetermination and residual life prediction coupling module is configured to determine the failure mode and predicting the residual life according to the time of occurrence of the exception, and transmit the failure mode and the residual life to the equipment maintenance decision-making platform;
the equipment failure mode predetermination and residual life prediction coupling system is implemented by an equipment failure mode predetermination and residual life prediction coupling method, which comprises:
acquiring, by the corresponding sensors, the equipment health information of the equipment to be detected;
receiving and caching the equipment health information and recognizing the time of occurrence of the exception of the equipment to be detected, by the corresponding abnormal state recognition module,
according to the time of occurrence of the exception of the equipment to be detected, determining the failure mode and predicting the residual life by the equipment failure mode predetermination and residual life prediction coupling module;
transmitting the failure mode and the residual life to the equipment maintenance decision-making platform; and
generating, by the equipment maintenance decision-making platform, a maintenance decision based on both the failure mode and the residual life predicted value with its confidence interval; and
displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff;
wherein the determining the failure mode and predicting the residual life comprises:
extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module;
calculating, by a probabilistic graphical model, a cumulative failure occurrence probability of the temporal sample;
according to the cumulative failure occurrence probability of the temporal sample, calculating an empirical distribution function of each probabilistic graphical model fragment, and taking a time and failure mode corresponding to a maximum value of the empirical distribution function as an equipment residual life predicted value and a failure mode predicted value respectively;
determining, by a Dvoretzky-Kiefer-Wolfowitz (DKW) inequality, a confidence interval of the equipment residual life predicted value, and calculating, by an edge density function, a variance of the failure mode predicted value; and
taking the equipment residual life predicted value, the failure mode predicted value, the confidence interval of the equipment residual life predicted value and the variance of the failure mode predicted value as prediction results of the equipment failure mode predetermination and residual life prediction coupling module;
the temporal sample xpq, is expressed as
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where,
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denote health information fragments of TCpq equipment nodes q of an equipment type p periodically collected at a time TCp;
a specific method for calculating the cumulative failure occurrence probability of the temporal sample comprises: moving rightwards, by the probabilistic graphical model, the probabilistic graphical model fragment corresponding to each element in a probabilistic graphical model set by one time slice, calculating a distance from a right end of each probabilistic graphical model fragment to a right end of the corresponding element and an accumulative occurrence probability of each failure mode until the distance from the night end of each probabilistic graphical model fragment to the right end of the corresponding element is zero, and determining the accumulative failure occurrence probability;
wherein, the probabilistic graphical model set is expressed as {Gp1, Gp2, …, GpkP}: where Gp1, Gp2, …, GpkP, denote probabilistic graphical models corresponding to Kp, failure modes of the equipment type p;
the accumulative occurrence probability F(lpk, k|Xpq) of each failure mode is calculated by:
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45
537
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where, lpq denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes a probability function, and k denotes each failure mode of the equipment type p;
the empirical distribution function Pr(RUL = lpk, k|Xpq) of each probabilistic graphical model fragment is expressed as:
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25
625
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where, lpk denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes the probability function, k denotes each failure mode of the equipment type p, F(.) denotes the probability function, Tp denotes the number of time slices in the probabilistic graphical model, and Tcp denotes a periodical extraction time.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above (including mathematical expressions associated therewith) and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a system and claim 2 recites a method, and each therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations) and the mental processes category (including an observation, evaluation, judgment, opinion). MPEP § 2106.04(a)(2).
“When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017) (determining that the claims to a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform did not merely recite "the abstract idea of using ‘mathematical equations for determining the relative position of a moving object to a moving reference frame’."). For example, a limitation that is merely based on or involves a mathematical concept described in the specification may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim.” MPEP § 2106.04(a)(2).
The recited functions:
“according to the time of occurrence of the exception of the equipment to be detected, determining the failure mode and predicting the residual life”
“wherein the determining the failure mode and predicting the residual life comprises:”
“calculating, by a probabilistic graphical model, a cumulative failure occurrence probability of the temporal sample;
according to the cumulative failure occurrence probability of the temporal sample, calculating an empirical distribution function of each probabilistic graphical model fragment, and taking a time and failure mode corresponding to a maximum value of the empirical distribution function as an equipment residual life predicted value and a failure mode predicted value respectively;
determining, by a Dvoretzky-Kiefer-Wolfowitz (DKW) inequality, a confidence interval of the equipment residual life predicted value, and calculating, by an edge density function, a variance of the failure mode predicted value; and
taking the equipment residual life predicted value, the failure mode predicted value, the confidence interval of the equipment residual life predicted value and the variance of the failure mode predicted value as prediction results of the equipment failure mode predetermination and residual life prediction coupling module;
the temporal sample xpq, is expressed as
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37
227
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37
108
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where,
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34
256
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denote health information fragments of TCpq equipment nodes q of an equipment type p periodically collected at a time TCp;
a specific method for calculating the cumulative failure occurrence probability of the temporal sample comprises: moving rightwards, by the probabilistic graphical model, the probabilistic graphical model fragment corresponding to each element in a probabilistic graphical model set by one time slice, calculating a distance from a right end of each probabilistic graphical model fragment to a right end of the corresponding element and an accumulative occurrence probability of each failure mode until the distance from the night end of each probabilistic graphical model fragment to the right end of the corresponding element is zero, and determining the accumulative failure occurrence probability;
wherein, the probabilistic graphical model set is expressed as {Gp1, Gp2, …, GpkP}: where Gp1, Gp2, …, GpkP, denote probabilistic graphical models corresponding to Kp, failure modes of the equipment type p;
the accumulative occurrence probability F(lpk, k|Xpq) of each failure mode is calculated by:
PNG
media_image4.png
45
537
media_image4.png
Greyscale
where, lpq denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes a probability function, and k denotes each failure mode of the equipment type p;
the empirical distribution function Pr(RUL = lpk, k|Xpq) of each probabilistic graphical model fragment is expressed as:
PNG
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25
625
media_image5.png
Greyscale
where, lpk denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes the probability function, k denotes each failure mode of the equipment type p, F(.) denotes the probability function, Tp denotes the number of time slices in the probabilistic graphical model, and Tcp denotes a periodical extraction time,” in claim 1 are determined by the Examiner as falling within the mathematical relationships and mathematical calculations sub-categories of mathematical concepts (MPEP 2106.04(a)(2)) because these functions are, as set forth in the claim and described in Applicant’s specification, each fundamentally characterized by mathematical operations or otherwise apply mathematical methods.
The recited functions:
“acquiring” “the equipment health information of the equipment to be detected;
receiving” “the equipment health information and recognizing the time of occurrence of the exception of the equipment to be detected,”
“according to the time of occurrence of the exception of the equipment to be detected, determining the failure mode and predicting the residual life,” and
“generating” “a maintenance decision based on both the failure mode and the residual life predicted value and associated confidence interval”
may be performed as mental processes. Acquiring or otherwise receiving equipment health information and recognizing a time of occurrence of an exception of the equipment may be performed via mental processes (e.g., observation and evaluation/judgment applied to observed equipment health information). Determining a failure mode and predicted a residual life according to a time(s) at which exception(s) occur may also be performed via mental processes (e.g., evaluation and judgment/opinion). Generating a maintenance decision based on both failure mode and a residual life predicted value with an associated confidence interval may also be performed via mental processes (e.g., evaluation of failure mode and residual life data and judgement in determining when/how to implement maintenance for equipment).
The type of high-level information analysis and deduction recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application 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 the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, the additional elements including “an equipment maintenance network and an equipment maintenance decision-making platform which are in communication connection with each other,” “the equipment maintenance network comprises equipment nodes, sensor sets, abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module; each of the sensor sets comprises a plurality of sensors,” “caching” the equipment health information, “transmitting the failure mode and the residual life to the equipment maintenance decision-making platform,” “extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module,” and “displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff” individually or in any combination do not appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted steps or a device for implementing the highlighted steps such as a generic computer.
More specifically, “an equipment maintenance network and an equipment maintenance decision-making platform which are in communication connection with each other,” “the equipment maintenance network comprises equipment nodes, sensor sets” “each of the sensor sets comprises a plurality of sensors” represent high-level data collection and processing structures (networked data processing system connected to sensors). The structures “abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module,” appear to be conventional computing platforms for processing information to implement the functions found to fall within the judicial exception. Similarly, “caching” the equipment health information, “transmitting the failure mode and the residual life to the equipment maintenance decision-making platform,” “extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module,” represent conventional computer processing tasks for collecting, storing, and processing information to implement the functions found to fall within the judicial exception.
Referring specifically to the element added by amendment, “displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff” represents conventional computer activity using conventional components in terms of outputting results determined via the step of generating the maintenance decision, which falls within the judicial exception.
In sum, the recited additional elements are not configured in a manner that represents an improvement to the functioning of a computer processing system or otherwise represents an improvement in a particular technology.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements including “transmitting the failure mode and the residual life to the equipment maintenance decision-making platform” and “extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module” are recited at a very high level of generality and therefore may constitute a conventional output transmission within or from a computer and computer-implemented data collection rather than being combined with the abstract idea in a particularized manner of equipment health evaluation.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 1 does not include any such transformation or reduction. Instead, claim 1 as a whole entails a series of mathematical calculations integrated with steps that may be performed via mental processes with the additional elements failing to provide a meaningful integration of the abstract idea in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity (data collection and processing) that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, the additional elements represent extra-solution activity as explained in the Step 2A Prong Two analysis, and furthermore appear to be generic and well understood as evidenced by the disclosures of Garvey (US 2009/0299654 A1) and Noda (US 2015/0160098 A1), each of which teaches,
“an equipment maintenance network (Garvey: FIG. 1 depicting network comprising downhole tool 20 coupled by telemetry (arrow) to surface processing unit 24, [0039], [0068] network configured to collect and process maintenance data. Noda: FIG. 3 depicting health management system 1 comprising health state prediction unit 13 and RUL prediction unit 14) and an equipment maintenance decision-making platform (Garvey: [0068] disclosing decision whether or not to perform maintenance (system is configured to include maintenance decision making platform). Noda: FIG. 3 system 1 includes output unit 15 that per [0082] outputs data to upper systems such as an asset health management) which are in communication connection with each other (Garvey: decision described in [0068] inherently requires communication of maintenance-related data (e.g., output of detector 44) to the system entity that responds by undertaking or not undertaking maintenance action. Noda: FIG. 3 health state prediction unit 13 and RUL prediction unit 14 are communicatively coupled with output unit 15),” “the equipment maintenance network comprises equipment nodes (Garvey: FIG. 1 depicted network includes drill string 11 and borehole assembly 18 that comprises downhole tool 20. Noda: FIGS. 2 and 3 depicting overall network as including equipment nodes), sensor sets (Garvey: FIG. 1 depicted network includes sensors 22. Noda: FIGS. 2 and 3 depicting data collection (FIG. 2) and receiving sensor data (FIG. 3), [0062]), abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module (Garvey: FIG. 1 depicted network includes surface processing unit 24 that per FIG. 3 includes predictor 42, detector 44, diagnose 46, and prognoser 48. Noda: FIG. 3 health state prediction unit 13 and RUL prediction unit 14); each of the sensor sets comprises a plurality of sensors (Garvey: FIG. 1 sensors 22 configured to acquire data associated with downhole tool 20, [0029] sensors acquire data associated with various drill string locations. Noda: [0062]),” “caching” the equipment health information (Garvey: FIG. 3 predictor 42 and detector 44 configured to receive and process (and therefore at least temporarily store) equipment health information including operations data 36 and maintenance data 38 such as in the form of measured observations, [0040]. Noda: FIG. 3 sensor data 12a stored/cached within time-series storage 12), “transmitting the failure mode and the residual life to the equipment maintenance decision-making platform (Garvey: FIG. 6 system configured to output alarm regarding abnormal operation, FIG. 8 diagnoser configured to output fault types, FIG. 10 prognoser configured to output RUL estimates. Noda: FIG. 3 output from health state prediction unit 13 and RUL prediction unit 14 output to output unit 15),” and “extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module (Garvey: [0118]-[0119] time series analysis utilized for failure predictions; FIG. 11 depicting degradation signals and associated TTF (RUL) determinations associated with time intervals, [0092]. Noda: FIG. 22 health state and RUL data determined in association with time intervals). Each of Garvey and Node further disclose a system configured to display output processing results (Noda: FIG. 3 output unit 15; Garvey: [0031] and [0035] computer system includes output device for providing processed results, FIG. 2 depicting computer system including a display).
Claim 1 is therefore not patent eligible under 101.
Claim 2 is substantially similar to and includes the same elements that render claim 1 ineligible subject matter and includes no further additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), or result in claim 2 as a whole amounting to significantly more than the judicial exception for the same reasons as discussed with regards to claim 1. Claim 2 is therefore also not patent eligible under 101.
Claims 3-6, depending from claim 2, provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of claim 1 (Step 2A, Prong One). None of dependent claims 3-6 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and each fails the “significantly more” test under the step 2B for the same reasons as discussed with regards to claim 1.
For example, claim 3 recites steps performed as part of implementing mathematical functions entailing mathematical relations and calculations and therefore falls within the mathematical concepts judicial exception.
Claim 4 recites a step of receiving and caching the equipment health information at a time, which represents high-level data gathering that constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. Claim 4 further recites prediction by an ARIMA prediction model, which entails mathematical relations/calculations and therefore falls within the mathematical concepts judicial exception. Claim 4 further recites determining the time of occurrence of the exception based on the results of the ARIMA prediction, which falls within the mental processes judicial exception because it can be performed via mental processes (e.g., evaluation of the ARIMA prediction and judgement to determine time of occurrence).
Claims 5 and 6 recite steps performed in formulating mathematical relations and calculations and therefore fall within the mathematical concepts exception.
Dependent claims 3-6 therefore also constitute ineligible subject matter under 101.
Subject Matter Patentably Distinguishable Over the Prior Arts
Claims 1-6 are found to be patentably distinguishable over the prior arts for the following reasons:
Regarding claim 1, substantially representative also of independent claim 2, the most pertinent prior arts are represented by Garvey (US 2009/0299654 A1) and Noda (US 2015/0160098 A1).
Regarding claim 1, Garvey discloses:
“An equipment failure mode predetermination and residual life prediction coupling system (FIG. 1 system 10; FIG. 2 system 30) comprising: an equipment maintenance network (FIG. 1 depicting network comprising downhole tool 20 coupled by telemetry (arrow) to surface processing unit 24, [0039], [0068] network configured to collect and process maintenance data) and an equipment maintenance decision-making platform ([0068] disclosing decision whether or not to perform maintenance (system is configured to include maintenance decision making platform)) which are in communication connection with each other (decision described in [0068] inherently requires communication of maintenance-related data (e.g., output of detector 44) to the system entity that responds by undertaking or not undertaking maintenance action), wherein the equipment maintenance network is configured to determine a failure mode and predict a residual life (FIG. 3 system includes predictor 42 and detector 44 for determining abnormal operation, diagnoser 46 for determining type of faults, and prognoser 48 for determining remaining useful life, [0040]) , and the equipment maintenance decision-making platform is configured to receive the failure mode and residual life of equipment and transmit the failure mode and residual life of the equipment to operation and maintenance staff (FIG. 6 system configured to output alarm regarding abnormal operation, FIG. 8 diagnoser configured to output fault types, FIG. 10 prognoser configured to output RUL estimates),
the equipment maintenance network comprises equipment nodes (FIG. 1 depicted network includes drill string 11 and borehole assembly 18 that comprises downhole tool 20), sensor sets (FIG. 1 depicted network includes sensors 22), abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module (FIG. 1 depicted network includes surface processing unit 24 that per FIG. 3 includes predictor 42, detector 44, diagnose 46, and prognoser 48);
each of the sensor sets comprises a plurality of sensors configured to acquire equipment health information of the corresponding equipment node (FIG. 1 sensors 22 configured to acquire data associated with downhole tool 20, [0029] sensors acquire data associated with various drill string locations);
each of the abnormal state recognition modules configured to receive and cache the equipment health information (FIG. 3 predictor 42 and detector 44 configured to receive and process (and therefore at least temporarily store) equipment health information including operations data 36 and maintenance data 38 such as in the form of measured observations, [0040]) and recognize a time of occurrence of an exception of equipment to be detected ([0032] sensor measurements are associated with time of measurements; [0049]);
the equipment failure mode predetermination and residual life prediction coupling module is configured to determine the failure mode and predict the residual life according to the time of occurrence of the exception ([0040] diagnose determines fault types, [0049] sensor data used for determining fault types includes time at which tool failed; FIG. 9 degradation paths 70 and lifetimes 72 data determined based on observed alarms 64 and class estimates (fault types) 68 and per FIG. 10 RUL estimates 74 determined based on degradation paths and lifetimes (therefore RULs are determined based on fault types that were determined based on time of exception, such that RULs are also determined, in part, based on time of exception), and transmit the failure mode and the residual life to the equipment maintenance decision-making platform (FIG. 6 system configured to output alarm regarding abnormal operation, FIG. 8 diagnoser configured to output fault types, FIG. 10 prognoser configured to output RUL estimates);
the equipment failure mode predetermination and residual life prediction coupling system is implemented by an equipment failure mode predetermination and residual life prediction coupling method, which comprises:
acquiring, by the corresponding sensors, the equipment health information of the equipment to be detected (FIG. 1 sensors 22 configured to acquire data associated with downhole tool 20, [0029] sensors acquire data associated with various drill string locations);
receiving and caching the equipment health information (FIG. 3 predictor 42 and detector 44 configured to receive and process (and therefore at least temporarily store) equipment health information including operations data 36 and maintenance data 38 such as in the form of measured observations, [0040]) and recognizing the time of occurrence of the exception of the equipment to be detected, by the corresponding abnormal state recognition module ([0032] sensor measurements are associated with time of measurements; [0049]);”
“according to the time of occurrence of the exception of the equipment to be detected, determining the failure mode and predicting the residual life by the equipment failure mode predetermination and residual life prediction coupling module ([0040] diagnose determines fault types, [0049] sensor data used for determining fault types includes time at which tool failed; FIG. 9 degradation paths 70 and lifetimes 72 data determined based on observed alarms 64 and class estimates (fault types) 68 and per FIG. 10 RUL estimates 74 determined based on degradation paths and lifetimes (therefore RULs are determined based on fault types that were determined based on time of exception, such that RULs are also determined, in part, based on time of exception);
transmitting the failure mode and the residual life to the equipment maintenance decision-making platform (FIG. 6 system configured to output alarm regarding abnormal operation, FIG. 8 diagnoser configured to output fault types, FIG. 10 prognoser configured to output RUL estimates);”
“wherein the determining the failure mode and predicting the residual life comprises:
extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module ([0118]-[0119] time series analysis utilized for failure predictions; FIG. 11 depicting degradation signals and associated TTF (RUL) determinations associated with time intervals, [0092]);
calculating, by a probabilistic graphical model, a cumulative failure occurrence probability of the temporal sample (FIG. 11 depicting a graphical model in which TTFs that per [0092) designate a probability of failure (per depiction in FIG. 11 the TTFs are cumulative over time); [0093]-[0094] cumulative probability of failure determined in accordance with a probability density function (Examiner notes that a probability density function output is a curve that constitutes a probabilistic graphical model)).”
Regarding “generating, by the equipment decision-making platform, a maintenance decision based on both the failure mode and the residual life predicted value with its confidence interval,” Garvey teaching using the failure mode and residual life to determine further diagnostic steps that may be related to further maintenance action ([0068] if no failure condition detected (operating normally) then no further maintenance action performed, and if failure condition detected further diagnostic steps performed based on residual prediction and fault data), and further teaches determining RUL (residual life) based on failure mode (FIG. 10 depicting degradation paths and associated lifetimes processed by prognoser 48 to estimate RUL, [0086]).
Using residual life (e.g., remaining useful life (RUL)) for scheduling maintenance was well known in the art prior to the effective filing data. For example, Noda discloses a system for managing equipment health (Abstract) that includes using RUL for scheduling maintenance ([0197]), and Garvey teaches generalized application of confidence intervals as statistical methods that may be applied to generated observation data ([0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Noda’s teaching of scheduling maintenance based on RUL to the system taught by Garvey in which RUL is generated in part based on failure mode and in which confidence intervals may be associated with observation data, such that in combination the system is configured to schedule maintenance based on an RUL that itself is based on a failure mode such that the maintenance scheduling is performed based on residual life with its confidence interval and on failure mode.
The motivation would have been to schedule maintenance for equipment based on optimal timing factors – the RUL itself and the condition (failure mode) indicating the RUL.
Regarding “displaying, by the equipment maintenance decision-making platform, the maintenance decision to the operational staff,” each of Garvey and Noda teach using a display to display processing outputs (Noda: FIG. 3 output unit 15; Garvey: [0031] and [0035] computer system includes output device for providing processed results, FIG. 2 depicting computer system including a display).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied either of Garvey’s or Noda’s teaching of using a computer display to display output to the system taught by Garvey as modified by Noda in which maintenance decisions are generated for presumable implementation such that in combination the system is configured to display the maintenance decision such as may be available to operators.
The motivation would have been to effectuate the generated maintenance decision via a known means of communication to human operators.
The prior arts of record, alone or in combination, also do not fairly teach or suggest that
“wherein the determining the failure mode and predicting the residual life” further includes:
“according to the cumulative failure occurrence probability of the temporal sample, calculating an empirical distribution function of each probabilistic graphical model fragment, and taking a time and failure mode corresponding to a maximum value of the empirical distribution function as an equipment residual life predicted value and a failure mode predicted value respectively;
determining, by a Dvoretzky-Kiefer-Wolfowitz (DKW) inequality, a confidence interval of the equipment residual life predicted value, and calculating, by an edge density function, a variance of the failure mode predicted value; and
taking the equipment residual life predicted value, the failure mode predicted value, the confidence interval of the equipment residual life predicted value and the variance of the failure mode predicted value as prediction results of the equipment failure mode predetermination and residual life prediction coupling module;
the temporal sample xpq, is expressed as
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where,
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denote health information fragments of TCpq equipment nodes q of an equipment type p periodically collected at a time TCp;
a specific method for calculating the cumulative failure occurrence probability of the temporal sample comprises: moving rightwards, by the probabilistic graphical model, the probabilistic graphical model fragment corresponding to each element in a probabilistic graphical model set by one time slice, calculating a distance from a right end of each probabilistic graphical model fragment to a right end of the corresponding element and an accumulative occurrence probability of each failure mode until the distance from the night end of each probabilistic graphical model fragment to the right end of the corresponding element is zero, and determining the accumulative failure occurrence probability;
wherein, the probabilistic graphical model set is expressed as {Gp1, Gp2, …, GpkP}: where Gp1, Gp2, …, GpkP, denote probabilistic graphical models corresponding to Kp, failure modes of the equipment type p:
the accumulative occurrence probability F(lpk, k|Xpq) of each failure mode is calculated by:
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where, lpq denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes a probability function, and k denotes each failure mode of the equipment type p;
the empirical distribution function Pr(RUL = lpk, k|Xpq) of each probabilistic graphical model fragment is expressed as:
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where, lpk denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(.) denotes the probability function, k denotes each failure mode of the equipment type p, F(.) denotes the probability function, Tp denotes the number of time slices in the probabilistic graphical model, and Tcp denotes a periodical extraction time” taken in combination with the other limitations of claim 1.
Independent claim 2 includes substantially the same features that distinguish claim 1 from the prior arts and is distinguishable over the prior art for the same reasons.
Claims 3-6 depend from claim 2 and are likewise distinguishable over the prior arts for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857