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
The amendments filed 25 July 2025 have been entered. Claims 1-20 remain pending. Claims 1, 3-4, 10, 16, & 18-19 have been amended.
Response to Arguments
Regarding “Claim Rejections – 35 U.S.C. §101”:
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 7 of 11 line 15 to page 8 of 11 line 12, filed 25 July 2025, with respect to Claim Rejections - 35 U.S.C. § 101 have been fully considered but they are not persuasive.
The Applicant argues that (page 8 of 11 lines 3-4):
“According to the specification, the disclosed system extends the life of the industrial equipment by acting to control degradation.”
& (page 8 of 11 lines 9-12):
“The controlling reflects the improvement described in the specification with respect to Figure 6 of the application as filed. As a result, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception.”
The Examiner respectfully responds that:
Rule:
See MPEP 2106.04(d)(I): “The courts have also identified limitations that did not integrate a judicial exception into a practical application: Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)”
Analysis:
The limitation(s) directed towards “controlling” (i.e., “and controlling degradation at the industrial equipment through preventative maintenance executed to extend a useful life of the industrial equipment based on the overall equipment effectiveness metric.”) is equivalent to “reciting the words "apply it" (or an equivalent) with the judicial exception”. As such, the amended claims have the same 35 USC § 101 issue(s) as shown in the non final office action filed 28 April 2025 but with the addition of a limitation effectively stating “apply it”.
Conclusion:
Therefore the amendment does not “Integrate the judicial exception into a practical application” at Revised step 2A prong two nor “amount to significantly more than the judicial exception” at step 2B. The claims 1-20 are not patentable under 35 U.S.C. § 101.
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 8 of 11 line 13 to line 24, filed 25 July 2025, with respect to Claim Rejections - 35 U.S.C. § 101 have been fully considered but they are not persuasive.
The Applicant argues that (page 8 of 11 lines 13-15):
“The present claims are patent eligible for reasons similar to those provided with respect to claim 3 of Example 47 (Anomaly Detection) found in the updated Section 101 guidance issued on July 17, 2024.”
& (page 8 of 11 lines 21-24):
“Similarly, the present claims are in improvement in the technical field of predicting equipment effectiveness. Accordingly, the present claims integrate any abstract ideas into a practical application and are patent eligible.”
The Examiner respectfully responds that:
Rule:
See MPEP 2106.05(g): “In Flook, the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula".”
See MPEP 2106.04(d)(1): “A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not "directed to" the recited judicial exception.”
Analysis:
The claim recites a judicial exception(s) with generic computer elements and instructions to effectively “apply it”. The claim does not include elements or steps that amount to significantly more than the judicial exception(s). The claim does not “recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application … by going beyond generally linking the use of the judicial exception to a particular technological environment”.
Conclusion:
The claims 1-20 are not patentable under 35 U.S.C. § 101.
Note: “AI-related SME examples 47-49 issued in 2024” is attached as it discloses “Example 47 (Anomaly Detection) found in the updated Section 101 guidance issued on July 17, 2024.”
Regarding “Claim Rejections – 35 U.S.C. §103”:
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 8 of 11 line 25 to page 9 of 11 line 21, filed 25 July 2025, with respect to Claim Rejections - 35 U.S.C. § 103 have been fully considered but they are not persuasive.
The Applicant argues that (page 9 of 11 lines 9-12):
“Even if Bechhoefer discloses a spectrum as asserted by the Examiner, which the Applicant does not concede, simply disclosing a spectrum does not teach or suggest anything regarding the content of the spectrum.”
& (page 9 of 11 lines 16-20):
“An estimated signal having an inner bearing fault represented as a frequency spectrum does not necessarily include “useful operational periods of time.” Accordingly, the Examiner fails to identify “the degradation data based on useful operational periods of time in the data comprising spectral features” in the cited references”
The Examiner respectfully responds that:
Rule:
See MPEP 2173.01(I): “During examination, a claim must be given its broadest reasonable interpretation consistent with the specification as it would be interpreted by one of ordinary skill in the art.”
See MPEP 2141(I): “When considering obviousness of a combination of known elements, the operative question is thus "whether the improvement is more than the predictable use of prior art elements according to their established functions.”
Analysis:
At least under the broadest reasonable interpretation,
1:a spectrum includes “content” and disclosure of the spectrum then necessarily includes disclosure of the “content”.
2: There would be no purpose in disclosing or creating or collecting the data for a spectrum if that data were not collected from a “useful operational periods of time”.
Conclusion:
At least under the broadest reasonable interpretation of the claims and in accordance with 35 U.S.C. § 103, the prior art of ‘US 1144244 B2 (Lavid Ben Lulu) in view of US 6651012 B1 (Bechoefer, cited in IDS filed 24 March 2023)’ or ‘US 1144244 B2 (Lavid Ben Lulu) in view of US 6651012 B1 (Bechoefer, cited in IDS filed 24 March 2023) in further view of US 20040111237 A1 (Vlok)’ teaches the claim limitations of claims 1-20.
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 9 of 11 line 22 to page 10 of 11 line 13, filed 25 July 2025, with respect to Claim Rejections - 35 U.S.C. § 103 have been fully considered but they are not persuasive.
The Applicant argues that (page 9 of 11 lines 27-28):
“The Examiner fails to show that the strong tones of Bechhoefer are equivalent to any particular spectral features of the spectrogram.”
& (page 10 of 11 lines 2-3):
“The Examiner fails to explain how strong tones that are associated with localized defects in Bechhoefer, encompass the useful operational periods of time recited by the claims.”
The Examiner respectfully responds that:
Rule:
See MPEP 2173.01(I): “During examination, a claim must be given its broadest reasonable interpretation consistent with the specification as it would be interpreted by one of ordinary skill in the art.”
See MPEP 2141(I): “When considering obviousness of a combination of known elements, the operative question is thus "whether the improvement is more than the predictable use of prior art elements according to their established functions.”
Analysis:
A spectrogram shows spectral features, a “strong tone” is a spectral feature that is above some amplitude and is therefore an easily detectable spectral feature of the spectrogram.
&
US 6651012 B1 (Bechoefer, cited in IDS filed 24 March 2023) Column 10 lines 58-60: “Localized defects tend to increase the energy levels of the strong tones. This indicator is designed to provide an overall indication of localized defects.”. The application discloses using “strong tones” and the data pertaining to these strong tones was necessarily gathered at some operation period of time, so Bechoefer discloses “strong tones” and “useful operational periods of time”.
Conclusion:
At least under the broadest reasonable interpretation of the claims and in accordance with 35 U.S.C. § 103, the prior art of ‘US 1144244 B2 (Lavid Ben Lulu) in view of US 6651012 B1 (Bechoefer, cited in IDS filed 24 March 2023)’ or ‘US 1144244 B2 (Lavid Ben Lulu) in view of US 6651012 B1 (Bechoefer, cited in IDS filed 24 March 2023) in further view of US 20040111237 A1 (Vlok)’ teaches the claim limitations of claims 1-20.
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.
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Flow Diagrams from MPEP 2106(III) & 2106.04(II)(A)
Claim 1:
Step
Analysis
Step 1:Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, Law of Nature, or Natural Phenomenon?
Yes; the claim recites:
“determining, with the at least one hardware processor, a probability of survival by fitting at least one degradation function to degradation data associated with the industrial equipment, the degradation data based on useful operational periods of time in the data comprising spectral features” see MPEP 2106.04(a)(2): “MATHEMATICAL CONCEPTS”
“predicting, with the at least one hardware processor, an overall equipment effectiveness metric as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models, wherein the predicted performance and the predicted quality are based on the probability of survival, and the overall equipment effectiveness metric identifies productivity of the industrial equipment at future points in time.” See MPEP 2106.04(a)(2)(III)(C)(1): “Performing a mental process on a generic computer”.
Revised Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; the claim further recites the additional elements of (extra solution activity):
“obtaining, with at least one hardware processor, data associated with industrial equipment, wherein the data comprises spectral features corresponding to different frequencies of sensor data for the industrial equipment” see MPEP 2106.04(d)(2)(c) “Whether The Limitation(s) Are Merely Extra-Solution Activity Or A Field Of Use”
The claim has been amended with the limitation of:
“and controlling degradation at the industrial equipment through preventative maintenance executed to extend a useful life of the industrial equipment based on the overall equipment effectiveness metric.”
Rule:
See MPEP 2106.04(d)(I): “The courts have also identified limitations that did not integrate a judicial exception into a practical application: Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)”
Analysis:
This amended limitation is not significantly more than saying “apply it”.
Conclusion:
This limitation does not integrate the judicial exception into a practical application at revised step 2A prong two.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than conventional data collection:
“obtaining, with at least one hardware processor, data associated with industrial equipment, wherein the data comprises spectral features corresponding to different frequencies of sensor data for the industrial equipment”. To establish generic nature of ‘obtaining data’, see MPEP 2106.05(d)(II): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
And, generic elements (see MPEP 2106.05(I)(A): “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception”). To establish generic nature of “industrial equipment”, See:
1) US 11442444 B2 (Lavid Ben Lulu) “System And Method For Forecasting Industrial Machine Failures” see Fig. 4 box S470 “probability for a forthcoming machine failure” (industrial equipment = industrial machine)
2) US 20040111237 A1 (Vlok) “Method For Estimating Residual Life Of Industrial Equipment”.
See title “Industrial Equipment”.
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 2:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 2 additionally recites:
“wherein the at least one degradation function is a Weibull degradation function, a linear degradation function, or combination of the Weibull degradation function and the linear degradation function”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I).
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“wherein the at least one degradation function is a Weibull degradation function, a linear degradation function, or combination of the Weibull degradation function and the linear degradation function”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further “Mathematical Concepts”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 3:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 3 additionally recites:
“wherein the data comprising spectral features is presented to a user as a spectrogram, the spectrogram comprising a visual representation of the data corresponding to operation periods of the industrial equipment”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“wherein the data presented to the user as a spectrogram, the spectrogram comprising a visual representation of the data corresponding to operation periods of the industrial equipment”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further “Mathematical Concepts”.
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 4:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 3 and thereby claim 1.
Claim 4 additionally recites:
“comprising determining the useful operational periods of time according to a threshold, wherein the threshold is a median of a root mean square (RMS) of spectral features of the spectrogram.”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“comprising determining the useful operational periods of time according to a threshold, wherein the threshold is a median of a root mean square (RMS) of spectral features of the spectrogram.”.
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further “Mathematical Concepts”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 5:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 5 additionally recites:
“comprising determining a remaining useful life of the industrial equipment based on a current health status of the industrial equipment and a minimum acceptable health status of the industrial equipment.”.
These are mental processes see MPEP 2106.04(a)(2)(III) .
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“comprising determining a remaining useful life of the industrial equipment based on a current health status of the industrial equipment and a minimum acceptable health status of the industrial equipment.”.
These are mental processes see MPEP 2106.04(a)(2)(III) .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further judicial exceptions of “mental processes”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 6:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 5 and thereby 1.
Claim 6 additionally recites:
“comprising presenting a survival plot to a user, the survival plot indicating at least one operational state of the industrial equipment over time.”
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“comprising presenting a survival plot to a user, the survival plot indicating at least one operational state of the industrial equipment over time.” These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further judicial exceptions of “Mathematical Concepts”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 7:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 6 and thereby 5 and thereby 1 and thereby 1.
Claim 7 additionally recites:
“wherein the presenting comprises visualizing the remaining useful life in real time by defining segments of the survival plot that correspond to predetermined operational states of the industrial equipment.”
These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“comprising presenting a survival plot to a user, the survival plot indicating at least one operational state of the industrial equipment over time.” These are “Mathematical Concepts” see MPEP 2106.04(a)(2)(I) & MPEP 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality … viii. Arranging transactional information on a graphical user interface…” .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further judicial exceptions of “Mathematical Concepts”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 8:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 8 additionally recites:
“comprising monitoring signatures in the data and comparing the signatures to known signatures in real time.”
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“comprising monitoring signatures in the data and comparing the signatures to known signatures in real time.” These are mental processes see MPEP 2106.04(a)(2)(III) .
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further judicial exceptions of “mental processes”:
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claim 9:
Step
Analysis
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes; the claim is directed towards a method which is a process and therefore one of the four statutory categories.
Revised Step 2A – Prong One: Does the claim recite an abstract idea, law of Nature, or Natural Phenomenon?
Yes; the claim recites:
The judicial exception(s) as inherited from claim 8 and thereby 1.
Claim 9 additionally recites:
“comprising: detecting one or more deviations between signatures in the data and the known signatures; and providing an alert to a user in response to a detected deviation.”
Revised Step 2A – Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No; The claim further recites the additional element(s):
“detecting one or more deviations between signatures in the data and the known signatures” These are mental processes see MPEP 2106.04(a)(2)(III) .
“and providing an alert to a user in response to a detected deviation.” These are extra solution activity see MPEP 2106.05(g) “Insignificant Extra-Solution Activity”
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No; the additional elements are no more than further judicial exceptions of “mental processes” & extra solution activity:
To establish generic nature of “providing an alert”, See:
1) US 11442444 B2 (Lavid Ben Lulu) “System And Method For Forecasting Industrial Machine Failures” see Fig. 4 box S480 “Generate a notification”
2) US 10776706 B2 (Zirnstein) “Cost-driven System And Method For Predictive Equipment Failure Detection” see Fig. 9 box 910 “transmit signal indicating whether maintenance is to be performed”.
Conclusion
Therefore, “Claim is not eligible subject matter under 35 USC 101”
Claims 10 through 20 are rejected for similar reasons as claims 1-9.
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.
Claim(s) 1, 3-16, & 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11442444 B2 (Lavid Ben Lulu) in view of US 6651012 B1 (Bechhoefer, cited in IDS filed 24 March 2023.
Regarding claim 1, Lavid Ben Lulu teaches a method, comprising: obtaining, with at least one hardware processor (Fig. 1 Machine Failure Predictor 140, column 10 lines 10-11: “The machine failure predictor 140 includes a processing circuitry 210”, hardware processor/(“processing circuitry 210”)), data associated with industrial equipment (Fig. 1 machine 170, column 4 lines 52-56: “Each sensor 120 is configured to collect sensory inputs such as, but not limited to, sound signals, ultrasound signals, light, movement tracking indicators, temperature, energy consumption indicators, and the like based on operation of the machine 170”, data/(“sensory inputs”) associated with industrial equipment/(“the machine”)), … ; determining, with the at least one hardware processor, a probability of survival by fitting at least one degradation function to degradation data associated with the industrial equipment (Fig. 4 “Determine a probability for a forthcoming machine failure” S470, probability of survival/(“probability … failure”)), … ; predicting, with the at least one hardware processor, an overall equipment effectiveness metric as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”), wherein the predicted performance and the predicted quality are based on the probability of survival, and the overall equipment effectiveness metric identifies productivity of the industrial equipment at future points in time (column 7 lines 47-53: “By applying the selected machine learning model, the machine failure predictor 140 may be configured to perform real-time, or near real-time, classification of a machine health state ... trend indicating on a forth-coming machine failure”); and controlling degradation at the industrial equipment through preventative maintenance executed to extend a useful life of the industrial equipment based on the overall equipment effectiveness metric (Fig. 1 – 140 “Machine Failure Predictor” & 160 ”Client”, column 4 lines 38-44: “The client device 160 may be, but is not limited to, a personal computer, a laptop, a tablet computer, a smartphone, a wearable computing device, or any other device capable of receiving and displaying notifications indicating maintenance and failure timing predictions, results of supervised analysis, unsupervised analysis of machine operation data, and the like.”).
Lavid Ben Lulu does not teach wherein the data comprises spectral features corresponding to different frequencies of sensor data for the industrial equipment; … the degradation data based on useful operational periods of time in the data comprising spectral features.
Bechhoefer does teach wherein the data comprises spectral features (Fig. 32 “Spectrum of Signal”) corresponding to different frequencies of sensor data for the industrial equipment (Fig. 32, different frequencies/(“sideband” & “ball passing”)); … the degradation data based on useful operational periods of time in the data comprising spectral features (Fig. 12 power spectrum decimation factor 214, method for predicting the health of a component makes use of spectral features)
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the device taught by Lavid Ben Lulu with the teachings of Bechhoefer. One would have added to the “method for forecasting industrial machine failures” using machine learning of Lavid Ben Lulu the method for “predicting heath of a component” using frequency spectrums of Bechhoefer. The motivation would have been that the combination would enable more efficient detection and prediction of the health of industrial machines (see US 11442444 B2 (Lavid Ben Lulu) column 2 lines 16-29: “existing solutions often rely on raw physical data that may not be indicative enough for enabling prediction of machine failures”, also see US 6651012 B1 (Bechhoefer) column 1 lines 33-42: disclosure is an “efficient technique for detecting part and device degradation”).
Regarding claim 3, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 1,
Bechhoefer further teaches wherein the data comprising spectral features is presented to a user (column 3 lines 54-58: “It should be noted that the collected data 18 may include data collected over a period of time from sensors such as 14a through 14c mounted on Machine 12. A user, such as a Pilot 26, may use a special service processor, such as the PPU24, connected to the Machine 12 to obtain different types of data”) as a spectrogram, the spectrogram comprising a visual representation of the data corresponding to operation periods of the industrial equipment (Fig. 32, column 3 lines 10-11: “FIG. 32 is a graphical representation of the signal of FIG. 31 as a frequency spectrum”, spectrogram/(“graphical representation of … frequency spectrum”)).
Regarding claim 4, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 3,
Bechhoefer further teaches comprising determining the useful operation periods of time according to a threshold, wherein the threshold is a median of a root mean square (RMS) of spectral features of the spectrogram (column 10 lines 27-29: “The Root-Mean-Square (RMS) value of the raw vibration amplitude represents the overall energy level of the vibration. The RMS value can be used to detect major overall changes in the vibration level”, column 10 lines 59-63: “This indicator is designed to provide an overall indication of localized defects. "Strong tones" are determined by applying a threshold which is set based on the mean of all the energy contents in the spectrum. Any tones that are above this threshold are attributed to this indicator.”).
Regarding claim 5, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 1,
Lavid Ben Lulu further teaches comprising determining a remaining useful life of the industrial equipment based on a current health status of the industrial equipment (column 7 lines 48-53: “the machine failure predictor 140 may be configured to perform real-time, or near real-time, classification of a machine health state. The classification may include, but is not limited to, three machine health states, such as, normal, trend indicating on a forthcoming machine failure”) and a minimum acceptable health status of the industrial equipment (column 7 lines 52-53: “machine health states, such as, normal, trend indicating on a forthcoming machine failure”, health status/(“machine health states”) including a minimum acceptable/(“normal”)).
Regarding claim 6, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 5,
Lavid Ben Lulu further teaches, the survival plot indicating at least one operational state of the industrial equipment over time (Fig. 3B, column 11 lines 51-56: “identify trends having influence on the machine behavior, and to predict machine failures in time. In the simulation shown in FIG. 3B, the correlation between the parameters, i.e., the indicative sensory inputs, may be that when the temperature, represented by the curve 310B, is below a certain threshold (not shown) the energy consumption, represented by the curve 320B, exceeds a certain threshold (not shown)”).
Bechhoefer further teaches comprising presenting a survival plot to a user(column 3 lines 54-58: “It should be noted that the collected data 18 may include data collected over a period of time from sensors such as 14a through 14c mounted on Machine 12. A user, such as a Pilot 26, may use a special service processor, such as the PPU24, connected to the Machine 12 to obtain different types of data”)
Regarding claim 7, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 6,
Lavid Ben Lulu further teaches wherein the presenting comprises visualizing the remaining useful life in real time (column 6 lines 41-45: “Indicative data feature may include, for example, operating temperature of a machine, current and recent energy consumption, speed of a mechanical component, and the like. Abnormal parameters include values of these features exceeding or falling below predetermined thresholds,”, real time/(“current”)) by defining segments of the survival plot that correspond to predetermined operational states of the industrial equipment (column 11 lines 57-63: “the energy consumption, represented by the curve 320B, exceeds a certain threshold (not shown). As an example, the correlation between the temperature and the energy consumption may indicate that when the temperature drops below a certain threshold and the energy consumption rises above a certain threshold, a machine failure is likely to occur within one week”, there is a predetermined threshold or segment of the graph for which when the curve representing data from a sensor falls below indicates an operational state such as ‘failure’).
Regarding claim 8, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 1,
Lavid Ben Lulu further teaches comprising monitoring signatures in the data (Fig. 4 “generate one or more data features” S430, column 7 lines 55-61: “The normal values may be previously determined based on indicators allowing to detect anomalies related to a certain sensory input, e.g., a data feature. A trend health state is represented by increasing changes associated with at least a portion of the plurality of indicative data features that may indicate on abnormal values of the indicative data features.”, signatures in the data/(“data features”)) and comparing the signatures to known signatures in real time (column 6 lines 41-45: “Indicative data feature may include, for example, operating temperature of a machine, current and recent energy consumption, speed of a mechanical component, and the like. Abnormal parameters include values of these features exceeding or falling below predetermined thresholds,”, real time/(“current”)).
Regarding claim 9, Lavid Ben Lulu in view of Bechhoefer teaches the method of claim 8,
Lavid Ben Lulu further teaches comprising: detecting one or more deviations between signatures in the data and the known signatures(Fig. 4 “generate one or more data features” S430, column 7 lines 55-61: “The normal values may be previously determined based on indicators allowing to detect anomalies related to a certain sensory input); and providing an alert to a user in response to a detected deviation (Fig. 1 client device 160, column 4 lines 38-44: “The client device 160 may be, but is not limited to, a personal computer, a laptop, a tablet computer, a smartphone, a wearable computing device, or any other device capable of receiving and displaying notifications indicating maintenance and failure timing predictions, results of supervised analysis, unsupervised analysis of machine operation data, and the like.”, providing an alert/(“receiving and displaying notifications”)).
Regarding claim 10, Lavid Ben Lulu teaches a system, comprising: at least one hardware processor(Fig. 1 Machine Failure Predictor 140, column 10 lines 10-11: “The machine failure predictor 140 includes a processing circuitry 210”, hardware processor/(“processing circuitry 210”)); and at least one computer-readable medium storing computer-executable instructions (Fig. 2 storage 230, column 10 lines 30-32: “computer readable instructions to implement one or more embodiments disclosed herein may be stored in the storage 230”); wherein the computer-executable instructions, when executed by the at least one hardware processor, cause the at least one hardware processor to: obtain data associated with industrial equipment (Fig. 1 machine 170, column 4 lines 52-56: “Each sensor 120 is configured to collect sensory inputs such as, but not limited to, sound signals, ultrasound signals, light, movement tracking indicators, temperature, energy consumption indicators, and the like based on operation of the machine 170”, data/(“sensory inputs”) associated with industrial equipment/(“the machine”)), … ; determine a probability of survival by fitting at least one degradation function to degradation data associated with the industrial equipment (Fig. 4 “Determine a probability for a forthcoming machine failure” S470, probability of survival/(“probability … failure”)), … ; predict an overall equipment effectiveness metric as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”), wherein the predicted performance and predicted quality are based on the probability of survival and the overall equipment effectiveness metric identifies productivity of the industrial equipment at future points in time (column 7 lines 47-53: “By applying the selected machine learning model, the machine failure predictor 140 may be configured to perform real-time, or near real-time, classification of a machine health state ... trend indicating on a forth-coming machine failure”) and control degradation at the industrial equipment through preventative maintenance executed to extend a useful life of the industrial equipment based on the overall equipment effectiveness metric (Fig. 1 – 140 “Machine Failure Predictor” & 160 ”Client”, column 4 lines 38-44: “The client device 160 may be, but is not limited to, a personal computer, a laptop, a tablet computer, a smartphone, a wearable computing device, or any other device capable of receiving and displaying notifications indicating maintenance and failure timing predictions, results of supervised analysis, unsupervised analysis of machine operation data, and the like.”).
Lavid Ben Lulu does not teach wherein the data comprises spectral features corresponding to different frequencies of sensor data for the industrial equipment … the degradation data based on useful operational periods of time in the data comprising spectral features.
Bechhoefer does teach wherein the data comprises spectral features (Fig. 32 “Spectrum of Signal”) corresponding to different frequencies of sensor data for the industrial equipment (Fig. 32, different frequencies/(“sideband” & “ball passing”)) … the degradation data based on useful operational periods of time in the data comprising spectral features (Fig. 12 power spectrum decimation factor 214, method for predicting the health of a component makes use of spectral features)
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the system taught by Lavid Ben Lulu with the teachings of Bechhoefer. One would have added to the “system … for forecasting industrial machine failures” using machine learning of Lavid Ben Lulu the apparatus for “predicting heath of a component” using frequency spectrums of Bechhoefer. The motivation would have been that the combination would enable more efficient detection and prediction of the health of industrial machines (see US 11442444 B2 (Lavid Ben Lulu) column 2 lines 16-29: “existing solutions often rely on raw physical data that may not be indicative enough for enabling prediction of machine failures”, also see US 6651012 B1 (Bechhoefer) column 1 lines 33-42: disclosure is an “efficient technique for detecting part and device degradation”).
Regarding claim 11, Lavid Ben Lulu in view of Bechhoefer teaches the system of claim 10,
Lavid Ben Lulu further teaches wherein a first machine learning model of the trained machine learning models is trained to predict planned production time based on historical production data (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”, ‘forthcoming’ indicates that a time for failure is being predicted).
Regarding claim 12, Lavid Ben Lulu in view of Bechhoefer teaches the system of claim 11,
Lavid Ben Lulu further teaches wherein a second machine learning model of the trained machine learning models is trained to predict performance of the industrial equipment based on the probability of survival (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”, ) and historical performance data (column 4 lines 46-48: “The machine 170 may be any machine for which performance can be represented via sensory data”, system predicts performance).
Regarding claim 13, Lavid Ben Lulu in view of Bechhoefer teaches the system of claim 12,
Lavid Ben Lulu further teaches wherein a third machine learning model of the trained machine learning models is trained to predict quality of the industrial equipment based on the probability of survival (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”, ) and historical quality data (column 4 lines 19-21: “The selected machine learning model is applied to the plurality of indicative data features for at least determining the probability for a forthcoming machine failure.”, models are selected which are based on indicative features which would include quality data).
Regarding claim 14, Lavid Ben Lulu in view of Bechhoefer teaches the system of claim 10,
Lavid Ben Lulu further teaches comprising a mobile device with a display (Fig. 1 client device 160, column 4 lines 38-44: “The client device 160 may be, but is not limited to, a personal computer, a laptop, a tablet computer, a smartphone, a wearable computing device,),
Bechhoefer further teaches wherein the instructions cause the at least one hardware processor to render a spectral plot representing the data comprising spectral features at the display (Fig. 32, column 3 lines 10-11: “FIG. 32 is a graphical representation of the signal of FIG. 31 as a frequency spectrum”, spectrogram/(“graphical representation of … frequency spectrum”)).
Regarding claim 15, Lavid Ben Lulu in view of Bechhoefer teaches the system of claim 10,
Lavid Ben Lulu further teaches comprising at least one sensor that captures sensor data associated with the industrial equipment (Fig. 1 sensor 120-n & machine 170, industrial equipment/(“machine”))
Bechhoefer further teaches and the instructions cause the at least one hardware processor to convert the sensor data to the data comprising spectral features (Fig. 32, column 3 lines 10-11: “FIG. 32 is a graphical representation of the signal of FIG. 31 as a frequency spectrum”, spectrogram/(“graphical representation of … frequency spectrum”)).
Regarding claim 16, Lavid Ben Lulu teaches at least one non-transitory storage media storing instructions that (Fig. 2 storage 230, column 10 lines 30-32: “computer readable instructions to implement one or more embodiments disclosed herein may be stored in the storage 230”), when executed by at least one processor, cause the at least one processor to: obtain data associated with industrial equipment (Fig. 1 machine 170, column 4 lines 52-56: “Each sensor 120 is configured to collect sensory inputs such as, but not limited to, sound signals, ultrasound signals, light, movement tracking indicators, temperature, energy consumption indicators, and the like based on operation of the machine 170”, data/(“sensory inputs”) associated with industrial equipment/(“the machine”)), … ; determine a probability of survival by fitting at least one degradation function to degradation data associated with the industrial equipment (Fig. 4 “Determine a probability for a forthcoming machine failure” S470, probability of survival/(“probability … failure”)), … ; predict an overall equipment effectiveness metric as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models (column 2 lines 58-61: “applying the selected machine learning model to the plurality of indicative data features; and determining a probability for a forthcoming machine failure.”), wherein the predicted performance and the predicted quality are based on the probability of survival, and the overall equipment effectiveness metric identifies productivity of the industrial equipment at future points in time (column 7 lines 47-53: “By applying the selected machine l