DETAILED ACTION
This non-final office action is responsive to application 18/101,520 as submitted 25 Jan. 2023.
Claim status is currently pending and under examination for claims 1-20 of which independent claims are 1 and 11.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The application effective filing date is 01/25/2022.
Information Disclosure Statement
As required by MPEP 609(c), the applicant’s submissions of the Information Disclosure Statement dated 01/26/23 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by MPEP 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: claims recite “P-F curve” without introducing the acronym. Appropriate correction is required.
Claim Rejection – 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 1-10 are rejected under 35 U.S.C. 101 for being directed to non-statutory subject matter. Claim 1 is drawn to a “software tool” as explicitly set forth in the preamble which can be ‘software per-se’ ineligible subject matter under MPEP 2106.03. The additional limitations appear to further limit the software itself without structure being positively recited to preclude software only. While the elements do include an “industrial asset” this serves for scheduling maintenance events. Further, the “physical mechanism” under the broadest reasonable interpretation in context of the limitations are understood to mean data about a physical mechanism of failure rather than the actual physical mechanism itself. This is reasonable in light of 1) the preamble provides support that each element is a software element, 2) the “identified physical mechanism of failure” appears to merely be an “identification” that is accompanied by precise evidence about the physical mechanism of failure, and 3) the specification itself seems to suggest that the identification of the physical mechanism of failure is just a determination made by the software (see pars. [0039]). The specification does not define or describe the physical mechanism of failure, but only uses data about a physical mechanism. As such, claim 1 is considered drawn to software per-se which is ineligible subject matter and rejected accordingly. Claims 2-10 depend from claim 1 to include the same issue without curing eligibility. Therefore, claims 1-10 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within (or could be amended to fall within) one of the four statutory categories. It is noted in the case of claims 1-10 that if a claim could be amended to fall within a statutory category, then the analysis should proceed to determine whether such an amended claim would qualify as eligible. In this case, claims 1-10 might be amended as a computer-readable medium/article of manufacture or system/machine. Further, claims 11-20 are a method/process. As such, the analysis should proceed under MPEP 2106.03.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the enumerated grouping of abstract idea being “Mental Processes”, but for the recitation of a software tool and machine learning tool. More particularly, claims recite:
“at least one identified physical mechanism of failure for the at least one asset and at least one identified precise evidence for each identified physical mechanism of failure for the at least one asset” (Mental Process, observation e.g. specification [0037] “Reliability Engineer (RE) must understand the failure mode(s) to determine the identifiable physical mechanism(s) and the precise evidence (PMPE) by which the mechanisms are recognized”)
“a monitor for each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time to obtain multiple inputs for each identified precise evidence for each identified physical mechanism of failure for the at least one asset” (Mental Process, observation e.g. specification [0041] “manually inspecting conditions”)
“dynamically plotting a P-F curve based upon inputs including the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time” (Mental Process, evaluation aided by pen and paper or sketched chart)
“a schedule of maintenance events created based upon the dynamically plotted P-F curve of at least one asset” (Mental Process, judgment e.g. event-planning, calendar of events see specification [0041] “if the P-F interval equals eight weeks, then the inspection frequency should be 8 weeks/2, or 4 weeks… vendor contract to come in at a specified frequency”)
Focus of the claim concerns reliability centered maintenance (RCM) for scheduling maintenance events using P-F curve plotting based on monitoring and identifying failure evidence. These functions do not preclude mental performance which may be carried out be a human described as a Reliability Engineer per specification. Accordingly, claims recite at least mental processes which is an abstract idea enumerated under MPEP 2106.04(a)(2)(III).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
“software tool” MPEP 2106.05(f) mere instructions to apply an exception or merely a tool to perform the abstract idea
“machine learning based tool” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception
Balance of the claim concerns a software tool and machine learning tool. Both the software and machine learning serve as tools to perform the abstract idea and are recited at a high level of generality. The act of applying software and applying machine learning fails to recite details of how a solution to a problem is accomplished, providing only the idea of a solution or outcome. Accordingly, the claim remains drawn to an abstract idea and the additional elements are insufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and do not reveal an inventive concept. Particularly, the additional elements are as follows:
“software tool” MPEP 2106.05(f) mere instructions to apply an exception or merely a tool to perform the abstract idea. Particularly, software implemented on a requisite computer does not qualify as a particular machine under MPEP 2106.05(b) and does not impart substantive meaningful limitation under MPEP 2106.05(e).
“machine learning based tool” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception. Particularly, said extra-solution activity is a well-understood, routine and conventional (WURC) activity under MPEP 2106.05(d) as is further evidenced by Ochella et al., “Adopting machine learning and condition monitoring P-F curves…” as per [P.2 ¶1-2] “most popular approaches in recent time is the use of machine learning… The literature is awash with studies that use machine learning (ML) algorithms”
Significantly more is not furnished from the additional elements in the balance of the claim. Applying a software tool and machine learning tool does not elevate the claim as a whole to satisfy the test of particular transformation or demonstrate a technical solution. If the claim provides only a result-oriented solution, with insufficient detail for how it is accomplished, then the claims do not contain an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no evidence that the combination of elements improves the functioning of a technology. Their collective functions merely provide conventional implementation.
For the foregoing reasons, claim 1 is found to be patent ineligible. This rejection applies equally to independent claims 1 and 11 as well to dependent claims 2-10 and 11-20. Dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, or that they include additional elements which integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 2 and 12 disclose wherein the PF curve is used to establish a potential failure point and a PF interval predicted for the asset. This is to embellish the abstract idea which may include mental process of evaluation. For example, predicted PF interval may entail estimation, and establishing potential failure point may be projection or extrapolating data points on a graph. There are no further additional elements.
Dependent claims 3 and 13 disclose wherein PF interval predicted is used to modify inputs among alternatives of changing maintenance response time, work order priority, or data collection frequency. This is also considered part of the abstract idea being mental process of evaluation such as urgency planning. There are no additional elements.
Dependent claims 4 and 14 disclose wherein PF interval predicted is used to identify additional precise evidence parameters to be monitored. This is considered part of the abstract idea which includes mental process of observation/identifying. There are no additional elements.
Dependent claims 5 and 15 disclose wherein the machine learning tool uses multivariate regression analysis to capture effects. The machine learning is considered an additional element which amounts to adding insignificant extra-solution activity under MPEP 2106.05(g). Particularly, said extra-solution activity is a well-understood, routine and conventional activity as is evidenced by Ochella and/or Friedman “Multivariate Adaptive Regression Splines” circa 1991. The multivariate regression does not recite a particular training transformation and is simply used to capture effects of operational parameters. Accordingly, the additional element is insufficient to integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 6 and 8 as well as 16 and 18 disclose further including a range of asset behavior over various use cases is created from the PF curve plot based on inputs including monitoring evidence for each failure mechanism over time. The limitation is considered to embellish the abstract idea of mental process including observation to monitor and characterize behavior of some nondescript use cases. This could be simulation of virtual components being analytically graphed over wall-clock or calendar time. There are no additional elements.
Dependent claims 7 and 9-10 as well as 17 and 19-20 disclose limitations already addressed in claim 4 verbatim excepting dependency of claim tree. Nothing new is introduced and the combination of elements does not add anything which hasn’t already been considered.
Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no evidence that the combination of elements improves the functioning of a technology. Their collective functions merely provide conventional implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 8-14 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Regan et al., US PG Pun No 2008/0006379A1 hereinafter Regain, in view of
Ochella et al., “Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension” hereinafter Ochella.
With respect to claim 1, Regan teaches:
A reliability engineering software tool for scheduling maintenance events of at least one industrial asset {Regan Fig 6 screenshot P-F interval in CBM - condition based maintenance [0044] “CBM…software” for [0021-18] “reliability-centered maintenance (RCM)” with schedules [0018,58]} comprising:
at least one identified physical mechanism of failure for the at least one asset and at least one identified precise evidence for each identified physical mechanism of failure for the at least one asset {Regan [0019-20] “identify failure mode… detector provides the evidence” i.e. [0037] “physical evidence by which failure can be recognized” see Figs 5A/B:595 and 6:903 described e.g. [0099] “identify the potential failure condition and the function failure point” similar at [0087]. An asset can be [0097] “bearing wears due to normal use… detection of increased vibration” e.g. bearing of aircraft [0020]};
a monitor for each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time to obtain multiple inputs for each identified precise evidence for each identified physical mechanism of failure for the at least one asset {Regan [0094,97] “monitoring the potential failure of a failure mode… detection of increased vibration via a vibration monitoring device” vibrations physically characterize the failure mode of a bearing mechanism [0037,20] and is evidenced by detection and/or inspection [0097,72], the failure noted over time/date [0077,63] and is entered as inputs into GUI Figs 4-6};
However, Regan does not disclose plotting the P-F curve to fairly teach or suggest the following limitations which are met by Ochella:
a machine learning based tool dynamically plotting a P-F curve based upon inputs including the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time {Ochella Figs 2-3 P-F curve, [P.4 Sect2.1.2] “plotted against time will yield the potential failure or P-F curve” preceded “Based on the information gathered from predictive testing and inspection tasks” predictive testing entails machine learning so-titled particularly by way of regression model [P.8 Eqs.16-18], [P.10 Sect4.2.2] and/or clustering (k-means, hierarchical) [P.7 Tbl.2], Fig 6 with state indicators of condition based on sensors provide evidence, as does [P.4 Sect2.1.3] “inspection is conducted at point P2”. Failures are quantified at Eq.13 as a function of extracted features. The software is implemented using MATLAB [P.8 Sect3.1 Last¶], [P.10 Sect4.2.2 ¶2]}; and
a schedule of maintenance events created based upon the dynamically plotted P-F curve of at least one asset {Regan [P.4 Sect2.1.3] “P-F interval within the range (t - t2) to (t - t3), during which a maintenance intervention should be planned and implemented” emphasis planned is scheduling, similarly at [P.4 Tbl.1] scheduled restoration. See again Figs 2-3, esp. Fig 3 asset life extension (LE)}.
Ochella is directed to reliability centered maintenance with software implementation thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ P-F plotting according to the techniques of Ochella in combination to arrive at the invention as claimed for a motivation “the major contribution of this paper includes a unique attempt at combining a tool from RCM called the potential failure (P-F) curve and ML algorithms (e.g., data mining, k-means clustering) to prioritize vulnerable equipment for life-extension under an era of ubiquitous data. The study suggest a new technique of visualizing and exploring P-F curves” [P.2 ¶2].
With respect to claim 2, the combination of Regan and Ochella teaches the reliability software tool according to claim 1 wherein
the dynamically plotted P-F Curve is used to establish a potential failure point and a P-F interval predicted for the asset {Ochella discloses [P.4 Sect2.1.2] “potential failure point, P, in Fig. 2(a)” showing P and interval, again Fig 3 where PFIF is P-F interval factor, i.e. [P.12] “predicted PFIF” by Eq.3}.
With respect to claim 3, the combination of Regan and Ochella teaches the reliability software tool according to claim 2 wherein
P-F interval predicted by the tool is used to modify the inputs in the form of one of changing a maintenance response time, a work order priority, or a data collection frequency {Ochella [P.10 Last¶] “prioritize the most vulnerable equipment for life-extension actions” emphasis prioritize, hence title, where life-extension actions convey work orders to be prioritized, similar at [P.6 Last¶]. Also [P.4 Last2¶] “P-F interval… data acquisition frequencies”}.
With respect to claim 4, the combination of Regan and Ochella teaches the reliability software tool according to claim 3 wherein
P-F interval predicted by the tool is used to identify additional precise evidence parameters to be monitored {Ochella [P.4 Sect2.1.2] “parameters of the equipment being monitored” listed [Tbl.3]. The monitored parameters may be identified by sensor selection and/or feature selection described e.g. [P.7-8 Sect 3.1-3.2]}.
With respect to claim 8, the combination of Regan and Ochella teaches the reliability software tool according to claim 2 further including
a range of asset behavior over varied use cases is created from the dynamically plotting a P-F curve based upon inputs including the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time {Ochella Fig 3 asset where interval is range with PF curve shown, similar Fig 6 Asset Register for plurality of Equipments 1-m convey varied use cases which can range from Failed to Healthy with plots showing y-axis time}.
With respect to claim 9, the combination of Regan and Ochella teaches the reliability software tool according to claim 8 wherein
P- F interval predicted by the tool is used to identify additional precise evidence parameters to be monitored {Ochella [P.4 Sect2.1.2] “parameters of the equipment being monitored” listed [Tbl.3]. The monitored parameters may be identified by sensor selection and/or feature selection described e.g. [P.7-8 Sect 3.1-3.2]}.
With respect to claim 10, the combination of Regan and Ochella teaches the reliability software tool according to claim 2 wherein
P-F interval predicted by the tool is used to identify additional precise evidence parameters to be monitored {Ochella [P.4 Sect2.1.2] “parameters of the equipment being monitored” listed [Tbl.3]. The monitored parameters may be identified by sensor selection and/or feature selection described e.g. [P.7-8 Sect 3.1-3.2]}.
With respect to claim 11, the rejection of claim 1 is incorporated. The difference in scope being a method to perform limitations of software tool claim 1. Regan discloses [0018] “systems and methods for identifying and analyzing condition-based maintenance.” The remainder of the claim is rejected for the same rationale as claim 1.
With respect to claim 12, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 11, and further teaches the limitation of claim 2. Therefore, the rejection of claim 2 is applied to claim 12.
With respect to claim 13, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 12, and further teaches the limitation of claim 3. Therefore, the rejection of claim 3 is applied to claim 13.
With respect to claim 14, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 13, and further teaches the limitation of claim 4. Therefore, the rejection of claim 4 is applied to claim 14.
With respect to claim 18, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 12, and further teaches the limitation of claim 8. Therefore, the rejection of claim 8 is applied to claim 18.
With respect to claim 19, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 18, and further teaches the limitation of claim 9. Therefore, the rejection of claim 9 is applied to claim 19.
With respect to claim 20, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 12, and further teaches the limitation of claim 10. Therefore, the rejection of claim 10 is applied to claim 20.
Claims 5-7 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Regan and Ochella in view of Barton et al., US PG Pub No 2022/0038335A1 hereinafter Barton.
With respect to claim 5, the combination of Regan and Ochella teaches the reliability software tool according to claim 2 wherein the dynamically plotted P-F Curve is generated {Ochella Figs 2-3} however does not expressly disclose that the regression is “multivariate” regression which is disclosed by Barton:
the machine learning based tool utilizes multivariate regression analysis to capture the effects of all input parameters of a failure mode {Barton [0060] “perform multivariate regression on the array of input data using a pre-trained machine learning model. Doing so allows script 502 to predict whether IoT node 132 is likely to fail, given its reported temperature, vibration and rotation” shown Fig 5:502}.
Barton is directed to failure prediction for maintenance thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify multivariate regression per Barton for Ochella’s regression as simple substitution among model types to yield predictable results and/or applying a known technique to a known method ready for improvement to yield predictable results. The model being pre-trained simplifies or reduces a level of experimentation needed by the skilled artisan and Barton notes that a technician is provided useful information regarding failures [0060].
With respect to claim 6, the combination of Regan, Ochella and Barton teaches the reliability software tool according to claim 5 further including
a range of asset behavior over varied use cases is created from the dynamically plotting a P-F curve based upon inputs including the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time {Ochella Fig 3 asset where interval is range with PF curve shown, similar Fig 6 Asset Register for plurality of Equipments 1-m convey varied use cases which can range from Failed to Healthy with plots showing y-axis time}.
With respect to claim 7, the combination of Regan and Ochella teaches the reliability software tool according to claim 5 wherein
P-F interval predicted by the tool is used to identify additional precise evidence parameters to be monitored {Ochella [P.4 Sect2.1.2] “parameters of the equipment being monitored” listed [Tbl.3]. The monitored parameters may be identified by sensor selection and/or feature selection described e.g. [P.7-8 Sect 3.1-3.2]}.
With respect to claim 15, the combination of Regan and Ochella teaches the software method for scheduling maintenance events according to claim 13, and further combination with Barton teaches the limitation of claim 5. Therefore, the rejection of claim 5 is applied to claim 15 with equal motivation.
With respect to claim 16, the combination of Regan, Ochella and Barton teaches the software method for scheduling maintenance events according to claim 15, and further teaches the limitation of claim 6. Therefore, the rejection of claim 6 is applied to claim 16.
With respect to claim 17, the combination of Regan, Ochella and Barton teaches the software method for scheduling maintenance events according to claim 15, and further teaches the limitation of claim 7. Therefore, the rejection of claim 7 is applied to claim 17.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hande et al., US PG Pub No 2020/0310397A1 see [0154] “prediction module 234 includes a maintenance module to predict a potential-to-functional failure interval (P-F interval)” includes graphing Figs 4, 8
Kumar et al., US PG Pub No 2015/0201918A1 see Fig 33 P-F curve/interval
Josebeck et Gowthan, “Demystifying the P-F Curve & Augmenting Machine Learning for Maintenance Optimization” inventor’s publication (not in IDS) has been reviewed
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chase P Hinckley whose telephone number is (571)272-7935. The examiner can normally be reached M-F 9:00 - 5:00.
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124