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 .
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0032] of the specification discloses that “FIG 2B illustrates a defect of defect size ddefect”, however figure 2B does not discloses a defect size, and instead discloses a reference “detect”.
Additionally, Fig. 2B uses different variables/symbols from those used in the specification.
Appropriate correction is required.
Claim Objections
Claim 14 objected to because of the following informalities:
A highest reliability coefficient should be the highest reliability coefficient, so it is clear that the second “reliability coefficient in the claim refers to the preceding instance. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 12-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 12 recites a predefined set of conditions associated with a nature of the bearing. It is not clear how the “nature” of the bearing is defined.
Claims 13-20 are rejected for their dependence on claim 12.
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 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 12 recites:
A computer-implemented method for estimating remaining useful life of a bearing, the method comprising:
receiving, by a processing unit, operational data associated with the bearing from at least one source in real-time wherein the operational data comprises a time-domain signal;
determining one or more failure modes associated with the bearing based on one or more features associated with the operational data, comprising:
analyzing the operational data using a plurality of signal processing pipelines,
wherein each of the signal processing pipelines is associated with a signal processing function and wherein the plurality of signal processing pipelines generates a response indicative of the one or more features associated with the operational data; and
classifying the one or more features from the response into one or more failure modes using a classification model;
identifying a best-suited virtual model corresponding to the bearing from a plurality of virtual models, wherein each of the virtual models indicates a behavior of the bearing for a predefined set of conditions associated with one or several of an application of bearing, a design of the bearing, a nature of the bearing, a damping design and a coupler design, further comprising:
updating the plurality of virtual models based on the one or more features and corresponding failure modes;
generating simulation instances based on each of the updated virtual models;
executing the simulation instances in a simulation environment to generate simulation results; and
identifying the best-suited virtual model based on the simulation results; and
computing a remaining useful life of the bearing using the best-suited virtual model.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “executing the simulation instances in a simulation environment to generate simulation results (solving the model equation); and
computing a remaining useful life of the bearing using the best-suited virtual model (calculation using model)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “determining one or more failure modes associated with the bearing based on one or more features associated with the operational data (determination), comprising:
analyzing the operational data using a plurality of signal processing pipelines,
wherein each of the signal processing pipelines is associated with a signal processing function and wherein the plurality of signal processing pipelines generates a response indicative of the one or more features associated with the operational data (analysis of data); and
classifying the one or more features from the response into one or more failure modes using a classification model (grouping or sorting of data);
identifying a best-suited virtual model corresponding to the bearing from a plurality of virtual models, wherein each of the virtual models indicates a behavior of the bearing for a predefined set of conditions associated with one or several of an application of bearing, a design of the bearing, a nature of the bearing, a damping design and a coupler design (making a determination), further comprising:
updating the plurality of virtual models based on the one or more features and corresponding failure modes (inputting features into model);
generating simulation instances based on each of the updated virtual models (inputting parameters into model); and
identifying the best-suited virtual model based on the simulation results (making a determination);” are treated as belonging to mental process grouping.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 12: A computer-implemented method for estimating remaining useful life of a bearing, the method comprising: receiving, by a processing unit, operational data associated with the bearing from at least one source in real-time wherein the operational data comprises a time-domain signal.
The additional element in the preamble of “A method for estimating remaining useful life of a bearing” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Receiving operational data associated with the bearing from at least one source in real-time wherein the operational data comprises a time-domain signal represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A computer and a processing unit (generic processor) are generally recited and are not qualified as particular machines. The Examiner notes that computer program product and computer-readable storage medium in claims 19 and 20 (described as non-transitory in the specification [0014]) are also generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 13-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 12, 13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tobon-Mejia et al. ("Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMM's"; Aerospace Conference, 2011 IEEE; pp.: 1-10; XP031938145; DOI: 10.1109/AERO.2011.5747561; ISBN: 978-1-4244-7350-2; 2011), hereinafter “TM” in view of Nair et al. (US 20220050015 A1), hereinafter “Nair”.
Regarding Claim 12, TM teaches a computer-implemented method for estimating remaining useful life of a bearing, the method comprising:
receiving, by a processing unit, operational data associated with the bearing from at least one source in real-time wherein the operational data comprises a time-domain signal (TM p. 6, Section 4. The failure prognostic method presented previously is tested on a rich condition monitoring data base taken from [34] and containing several bearings tested until the failure. […] The test data extracted from [34] correspond to several tests under constant conditions. Four bearings were installed on one shaft. The angular velocity was kept constant at 2000 rpm and a 6000 lb radial load was applied onto the shaft and bearings (Fig. 9). On each bearing two Accelerometer were installed for a total of 8 accelerometers (one vertical Y and one horizontal X) to register the accelerations generated by the vibrations, the sampling rate was fixed at 20 kHz. For simulation purposes (learning and on-line failure prognostic) twelve condition monitoring data histories are used (eleven for learning and one for test), each bearing was considered failed at the end of its associated history. );
identifying a best-suited virtual model corresponding to the bearing from a plurality of virtual models (TM p. 5, Col. 2, para 3 The first step consists in detecting the appropriate MoG HMM that best fits and represents the on-line observed sequence of nodal energy. Indeed, the features are continuously fed to the set of learned models (completely defined) and a likelihood is calculated in order to select the appropriate model (Fig. 6). The selected model is then used to compute the RUL.), wherein each of the virtual models indicates a behavior of the bearing for a predefined set of conditions associated with one or several of an application of bearing, a design of the bearing, a nature of the bearing, a damping design and a coupler design (TM p. 6, Col. 2, para. 1, The test data extracted from [34] correspond to several tests under constant conditions. Four bearings were installed on one shaft. The angular velocity was kept constant at 2000 rpm and a 6000 lb radial load was applied onto the shaft and bearings (Fig. 9). Data is based on application), further comprising:
updating the plurality of virtual models based on the one or more features and corresponding failure modes (TM p. 5, Col. 2, para. 3, the features are continuously fed to the set of learned models (completely defined));
generating simulation instances based on each of the updated virtual models (TM Fig. 6, Initial Conditions, Data Acquisition and Pre-processing);
executing the simulation instances in a simulation environment to generate simulation results (TM Fig. 6 WPD and Learning to Model Base); and
identifying the best-suited virtual model based on the simulation results (TM p. 5, Col. 2, para. 3, a likelihood is calculated in order to select the appropriate model (Fig. 6). The selected model is then used to compute the RUL. See Fig. 6 Model Selection); and
computing a remaining useful life of the bearing using the best-suited virtual model (TM p. 5,, Col. 2, para. 3, The selected model is then used to compute the RUL.).
TM is not relied upon to teach determining one or more failure modes associated with the bearing based on one or more features associated with the operational data;
analyzing the operational data using a plurality of signal processing pipelines,
wherein each of the signal processing pipelines is associated with a signal processing function and wherein the plurality of signal processing pipelines generates a response indicative of the one or more features associated with the operational data; and
classifying the one or more features from the response into one or more failure modes using a classification model.
Nair teaches a computer-implemented method for estimating remaining useful life of a bearing, the method comprising:
receiving, by a processing unit (Nair [0057] The processor 304 is configured to execute the defined computer program instructions in the modules. Further, the processor 302 is configured to execute the instructions in the memory 310 simultaneously.), operational data associated with the bearing from at least one source in real-time wherein the operational data comprises a time-domain signal (Nair [0112] The method begins at step 602 by receiving condition data associated with the operation of the bearing and a technical system housing the bearing. The condition data of the bearing/technical system is received from different sources (e.g., sensors, scanners, user devices, etc.). The sensors measure operating parameters associated with the technical system. Also see [0013] The condition data of the bearing is received from different sources (e.g., sensors, scanners, user devices, etc.). The sensors measure operating parameters associated with the technical system. The sensors may include vibration sensors, current and voltage sensors, etc.);
determining one or more failure modes associated with the bearing based on one or more features associated with the operational data (Nair [0116] At step 610, a defect model is generated based on the thermal model. The defect model includes defects that are detected in the condition data. At step 610, the condition data is compared with defect profiles to predict the defects.);
analyzing the operational data using a plurality of signal processing pipelines,
wherein each of the signal processing pipelines is associated with a signal processing function and wherein the plurality of signal processing pipelines generates a response indicative of the one or more features associated with the operational data (Nair [0045]-[0046], specifically, [0045] The current model 120 includes equivalent circuit model 122 generated based on the bearing specification and the technical specification. […] [0046] The thermal model 130 includes a heat model 132 based on the current model 120. The heat model 132 is a distribution of spark heat of at least one spark in time series.); and
classifying the one or more features from the response into one or more failure modes using a classification model (Nair [0048] The defect model 140 includes a defect profile model 142. The defect profile model 142 is configured to map the spark heat and the spark diameter to defect profiles. Also see [0094] The defect module 330 is configured to generate a defect model to determine a defect in the bearing 390 based on the bearing model. As used herein, the term “defect profile” refers to anomalous data represented as a function of operation environment, operation profile and/or load profile associated with the bearing and/or technical system. The defect model maps the processed data to locations on the bearing, thereby sorting the defects based on location).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify TM in view of Nair to teach determining one or more failure modes associated with the bearing based on one or more features associated with the operational data;
analyzing the operational data using a plurality of signal processing pipelines,
wherein each of the signal processing pipelines is associated with a signal processing function and wherein the plurality of signal processing pipelines generates a response indicative of the one or more features associated with the operational data; and
classifying the one or more features from the response into one or more failure modes using a classification model, to explicitly disclose the features and data being processed by the models of TM to ultimately determine the best model (TM p. 7, Conclusion, The method is based on the transformation of the data provided by the sensors installed to monitor the component into relevant models.).
Regarding Claim 13, TM in view of Nair (as stated above) further teaches wherein the operational data comprises at least one of a temperature signature, a vibration signature, a current signature and an angular velocity associated with the bearing (TM p. 6, Col. 2, para. 1, On each bearing two Accelerometer were installed for a total of 8 accelerometers (one vertical Y and one horizontal X) to register the accelerations generated by the vibrations, the sampling rate was fixed at 20 kHz. Also see Nair [0013] The condition data of the bearing is received from different sources (e.g., sensors, scanners, user devices, etc.). The sensors measure operating parameters associated with the technical system. The sensors may include vibration sensors, current and voltage sensors, etc.).
Regarding Claim 15, TM in view of Nair (as stated above) further teaches determining one or more key performance indicators associated with the bearing using the best-suited virtual model; and
computing the remaining useful life based on the one or more key performance indicators determined (TM p. 6, Col. 1, para. 1, Finally, in the fourth step the paths identified previously are used to estimate the RUL. This latter is obtained using the temporal parameters of the stay duration in each state. Remaining useful life is calculated based on the chosen parameters).
Regarding Claim 16, TM in view of Nair (as stated above) further teaches outputting the remaining useful life in real-time on an output device (Nair [0057] The apparatus 300 includes a communication unit 302, at least one processor 304, a display 306, a Graphical User Interface (GUI) 308 and a memory 310 communicatively coupled to each other. […] The processor 304 is configured to execute the defined computer program instructions in the modules. Further, the processor 302 is configured to execute the instructions in the memory 310 simultaneously. Also see [0098] The expended life, the remaining life and a usage profile of the bearing are rendered on the display 306 via the GUI 308).
Remaining Claim 17, TM in view of Nair (as stated above) further teaches An apparatus for estimating remaining useful life of a bearing, the apparatus comprising:
one or more processing units; and a memory unit communicatively coupled to the one or more processing units, wherein the memory unit comprises a bearing monitoring module stored in a form of machine-readable instructions executable by the one or more processing units, wherein the bearing monitoring module is configured to perform the method according to claim 12.(Nair [0057] The apparatus 300 includes a communication unit 302, at least one processor 304, a display 306, a Graphical User Interface (GUI) 308 and a memory 310 communicatively coupled to each other. […] The processor 304 is configured to execute the defined computer program instructions in the modules. Further, the processor 302 is configured to execute the instructions in the memory 310 simultaneously. Also see [0098] The expended life, the remaining life and a usage profile of the bearing are rendered on the display 306 via the GUI 308).
Regarding Claim 18, TM in view of Nair (as stated above) further teaches one or more sources configured for providing operational data associated with the bearing (TM p. 4, Col. 1, para. 1, The method is based on a nondestructive control and uses the data provided by sensors installed to monitor the component’s condition.); and
an apparatus according to claim 17, communicatively coupled to the one or more sources, wherein the apparatus is configured for estimating remaining useful life of the bearing based on the operational data (TM p. 4, Col. 1, para. 1, The method is based on a nondestructive control and uses the data provided by sensors installed to monitor the component’s condition. Also see para. 2, the raw data recorded by the sensors are processed. And Fig. 6, Data Acquisition).
Regarding Claim 19, TM in view of Nair (as stated above) further teaches a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method according to claim 12 (Nair [0057] The processor 304 is configured to execute the defined computer program instructions in the modules. Also see [0120] Embodiments may include a computer program product including program modules accessible from computer-usable or computer-readable medium storing program code for use by or in connection with one or more computers, processors, or instruction execution system.).
Regarding Claim 20, TM in view of Nair (as stated above) further teaches a computer-readable storage medium comprising instructions which, when executed by a data-processing system, causes the data-processing system to perform a method according to claim 12 (Nair [0057] The apparatus 300 includes a communication unit 302, at least one processor 304, a display 306, a Graphical User Interface (GUI) 308 and a memory 310 communicatively coupled to each other. […] The processor 304 is configured to execute the defined computer program instructions in the modules. Further, the processor 302 is configured to execute the instructions in the memory 310 simultaneously. Also see [0098] The expended life, the remaining life and a usage profile of the bearing are rendered on the display 306 via the GUI 308).
The Examiner notes that there is no prior art rejection for claim 14, as the TM reference only teaches calculating a confidence coefficient and a likelihood of the model, which both seem to be distinct from a reliability coefficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nair (WO 2021009042 A1) discloses Managing Health Condition Of A Rotating System.
Leohold et al. (Prognostic Methods for Predictive Maintenance: A generalized Topology, IFAC-PapersOnLine, Volume 54, Issue 1, 2021, Pages 629-634, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2021.08.073 discloses a literature review on the state-of-the-art prognostic methods is conducted to obtain their generalized topology and overview of the different kinds of comprised models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2857
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