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 .
Election/Restrictions
The restriction requirement of 06/04/2025 has been withdrawn as a result of petition decision of 09/16/2025.
Claim Objections
Claims 2-3 and 12-15 are objected to because of the following informalities:
In claim 2, line 10, there should be a conjunction “and” in the end of the line to correct a grammatical error.
The other claim(s) not discussed above are objected to for inheriting the issue(s) from their linking claim(s).
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1- 17 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.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim recites the limitations beginning from “preprocessing the acquired working condition data, condition monitoring data, and lubrication assessment data to obtain preprocessed working condition data, preprocessed condition monitoring data, and preprocessed lubrication assessment data” to the end of the claim. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional element of “acquiring working condition data related to target lubrication, condition monitoring data related to the target lubrication, and lubrication assessment data related to the target lubrication.” However, this is recited at a high level of generality to collect the data for the abstract idea, which is an insignificant extra-solution activity. See MPEP 2106.05(g). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element in the claim is an insignificant extra-solution activity for collecting the data for the abstract idea. Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claim 9 is similarly rejected by analogy to claim 1. The data collector and processor can be generic computer component to facilitate data collection and data processing. They are not sufficient to make the claim eligible. See MPEP 2106.05(d), (f), and (g).
Dependent claims 2-8 and 10-17 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea., as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the claims into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea. Note that claim 8 recites “optimizing the target lubrication based on the lubrication assessment result.” It is recited at a high level of generality that it can be an insignificant extra-solution activity or a mental process, such as putting the target lubrication in a watch list. It is still insufficient to make the claim eligible.
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.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al. ("Condition Monitoring of Engine Lubrication Oil of Military Vehicles: A Machine Learning Approach" 17th Australian Aerospace Congress, 26-28 February 2017; cited previously; hereinafter “Le”).
Regarding claims 1 and 10, Le teaches a lubrication assessment method (i.e., “we classify the condition of engine lubrication oil”; see Abstract) comprising:
acquiring working condition data related to target lubrication, condition monitoring data related to the target lubrication (i.e., “VHUMS sensor data are acquired… Up to 48 sensor fields are recorded, measuring different vehicle dynamics and conditions, such as engine RPM, engine temperature, throttle position, oil temperature, odometer, vehicle speed, fuel usage and ambient air temperature”), and lubrication assessment data related to the target lubrication (i.e., “The laboratory reports provide information on the physical properties and elemental concentrations in the lubrication oil taken at specific intervals”; see p. 3, ¶ 5);
preprocessing the acquired working condition data, condition monitoring data, and (i.e., “As a preprocessing step prior to classification… the VHUMS data set is first filtered to remove missing and abnormal samples, and the range of data features are limited to ensure an effective classification learning process”; see p. 4, ¶ 1);
performing data integration on the preprocessed working condition data, the preprocessed condition monitoring data, and the (i.e., “VHUMS and laboratory test report data are combined and correlated”; see p. 4, ¶ 1);
performing feature extraction on data in the integrated data set according to data types and data characteristics based on the integrated data set to obtain a feature data set related to the target lubrication (i.e., “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports”; see p. 4, ¶ 3);
establishing a lubrication analysis model for assessment of the target lubrication based on the feature data set related to the target lubrication (i.e., “(i.e., “we adopt machine learning to learn and predict lubrication oil condition based on sensory information from VHUMS and laboratory test reports”; see p. 3, ¶ 3); and
assessing the target lubrication and generating a lubrication assessment result, based on the lubrication analysis model (i.e., “As such, the outcome (oil condition) can be associated with the input features”; see p. 5, ¶ 1).
Le does not explicitly disclose (see only the underlined):
preprocessing the acquired working condition data, condition monitoring data, and lubrication assessment data to obtain preprocessed working condition data, preprocessed condition monitoring data, and preprocessed lubrication assessment data;
performing data integration on the preprocessed working condition data, the preprocessed condition monitoring data, and the preprocessed lubrication assessment data to obtain an integrated data set.
The difference is that the lubrication assessment data need to be preprocessed. However, preprocessing data, such as data cleaning, or outlier removing, is well-known and taught by Le in p. 4, ¶ 1 (i.e., preprocessing VHUMS data). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Le to also preprocess the lubrication assessment data by preprocessing the acquired working condition data, condition monitoring data, and lubrication assessment data to obtain preprocessed working condition data, preprocessed condition monitoring data, and preprocessed lubrication assessment data; performing data integration on the preprocessed working condition data, the preprocessed condition monitoring data, and the preprocessed lubrication assessment data to obtain an integrated data set, as claimed, The rationale would be to reduce the influence of noise in the data for better results.
Note that in claim 10, the computer and the storage medium are well-known and obvious for implementing computerized data processing methods.
Regarding claim 2, Le further teaches: wherein the performing feature extraction on data in the integrated data set according to the data types and the data characteristics based on the integrated data set to obtain the feature data set related to the target lubrication comprises:
extracting features of the working condition data based on the integrated data set to obtain working condition features (i.e., “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports… the initial feature set is selected based on variables described in the literature as well as from subject matter experts and engineering knowledge”; see p. 4, ¶ 3);
extracting features of the condition monitoring data based on the integrated data set to obtain condition monitoring features (i.e., “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports… the initial feature set is selected based on variables described in the literature as well as from subject matter experts and engineering knowledge”; see p. 4, ¶ 3);
extracting features of the lubrication assessment data based on the integrated data set to obtain lubrication assessment features (i.e., “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports… the initial feature set is selected based on variables described in the literature as well as from subject matter experts and engineering knowledge”; see p. 4, ¶ 3);
obtaining the feature data set related to the target lubrication based on the working condition features, the condition monitoring features, and the lubrication assessment features (see Table 1 on p. 4).
Regarding claim 3, Le further teaches: wherein the obtaining the feature data set related to the target lubrication based on the working condition features, the condition monitoring features, and the lubrication assessment features comprises:
obtaining fused feature data by a feature fusion processing based on the working condition features, the condition monitoring features, and the lubrication assessment features, and generating the feature data set related to the target lubrication based on the fused feature data (i.e., “The selected features are fed into PCA with the ranker search method [32]. The eigenvectors with lower eigenvalues (against a threshold) are removed, and an inverse transform to select the original features contributing to the key principal components is performed. The corresponding features identified from VHUMS data and laboratory test reports are shown in Table 1”; see p. 4, ¶ 3).
Regarding claim 4, Le further teaches: wherein the establishing the lubrication analysis model for assessment of the target lubrication based on the feature data set related to the target lubrication comprises:
establishing a lubrication anomaly detection model for detecting lubrication anomaly (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that degraded and unsuitable are the detected anomaly) based on the feature data set related to lubrication (i.e., “we adopt machine learning to learn and predict lubrication oil condition based on sensory information from VHUMS and laboratory test reports”; see p. 3, ¶ 3; “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports”; see p. 4, ¶ 3);
establishing a lubrication failure mode classification model for classifying lubrication failure modes (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that degraded and unsuitable are the classified failure modes) based on the feature data set related to lubrication (i.e., “we adopt machine learning to learn and predict lubrication oil condition based on sensory information from VHUMS and laboratory test reports”; see p. 3, ¶ 3; “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports”; see p. 4, ¶ 3);
establishing a lubrication level classification model for classifying lubrication levels (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that normal, degraded and unsuitable are the classified lubrication level) based on the feature data set related to lubrication (i.e., “we adopt machine learning to learn and predict lubrication oil condition based on sensory information from VHUMS and laboratory test reports”; see p. 3, ¶ 3; “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports”; see p. 4, ¶ 3); and
establishing a lubricating indicator prediction model for predicting lubricating indicators (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that normal, degraded and unsuitable are the predicted lubricating indicators) based on the feature data set related to lubrication (i.e., “we adopt machine learning to learn and predict lubrication oil condition based on sensory information from VHUMS and laboratory test reports”; see p. 3, ¶ 3; “a well-established method for feature selection and extraction, is used to identify a set of important features/variables from VHUMS data and laboratory test reports”; see p. 4, ¶ 3).
Regarding claims 5 and 11, Le further teaches: wherein the assessing the lubrication and generating the lubrication assessment result based on the lubrication analysis model comprises:
detecting lubrication anomalies and generating a lubrication anomaly detection result, based on outputs of the lubrication anomaly detection model (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that degraded and unsuitable are the detected anomaly);
classifying lubrication failure modes and generating a lubrication failure mode classification result, based on outputs of the lubrication failure mode classification model (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that degraded and unsuitable are the classified failure modes);
classifying lubrication levels and generating a lubrication level classification result, based on outputs of the lubrication level classification model (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that normal, degraded and unsuitable are the classified lubrication level);
predicting lubrication indicators and generating a lubrication indicator prediction result, based on the lubrication indicator prediction model (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that normal, degraded and unsuitable are the predicted lubricating indicators); and
generating a lubrication health assessment result based on at least one of the lubrication anomaly detection result, the lubrication failure mode classification result, the lubrication level classification result, and the lubrication indicator prediction result (i.e., “the lubrication oil condition is classified into three categories, namely normal, degraded, and unsuitable”; see p. 4, ¶ 2; note that normal, degraded and unsuitable are the health assessment result).
Note that in claim 11, the computer and the storage medium are well-known and obvious for implementing computerized data processing methods.
Regarding claim 6, Le further teaches:
wherein the preprocessing the acquired working condition data, condition monitoring data, and lubrication assessment data comprises performing at least one of data deduplication processing, data denoising processing, data encoding processing and data filtering processing (i.e., “As a preprocessing step prior to classification… the VHUMS data set is first filtered to remove missing and abnormal samples, and the range of data features are limited to ensure an effective classification learning process”; see p. 4, ¶ 1; see, also, discussion of claim 1 regarding preprocessing the lubrication assessment data).
Regarding claim 7, Le further teaches: wherein the performing data integration on the preprocessed working condition data, the preprocessed condition monitoring data, and the preprocessed lubrication assessment data comprises:
performing at least one of synchronization, alignment, and correction processing on the preprocessed working condition data, the preprocessed condition monitoring data, and the preprocessed lubrication assessment data (i.e., “VHUMS and laboratory test report data are combined and correlated”; see p. 4, ¶ 1).
Regarding claim 8, Le further teaches:
optimizing the target lubrication based on the lubrication assessment result (i.e., “This gives Defence the ability to develop a more precise and cost effective engine oil maintenance regime to be applied to the military vehicles, whereby over- and under-servicing of vehicles may be avoided”; see p. 2, ¶ 1; “change the oil” based on the result; see p. 3, ¶ 5).
Regarding claim 9, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings. Note that the data collector (which can be a computer interface for data communication) and the processor are well-known and obvious for implementing computerized data processing methods.
Regarding claim 12, the claim recites the same substantive further limitations as claim 4 and is rejected by applying the same further teachings.
Regarding claim 13, the claim recites the same substantive further limitations as claim 5 and is rejected by applying the same further teachings.
Regarding claim 14, the claim recites the same substantive further limitations as claim 6 and is rejected by applying the same further teachings.
Regarding claim 15, the claim recites the same substantive further limitations as claim 7 and is rejected by applying the same further teachings.
Regarding claim 16, Le further teaches:
wherein acquiring working condition data comprises acquiring real-time working condition data of an operating mechanical equipment (i.e., “engine RPM, engine temperature, throttle position, … odometer, vehicle speed”; see p. 3, ¶ 5), and
wherein acquiring condition monitoring data comprises acquiring real-time vibration data of the operating mechanical equipment and/or real time temperature data of the operating mechanical equipment and/or real time ferrous particle content of a lubricant of the operating mechanical equipment and/or real time moisture content of the lubricant of the operating mechanical equipment (i.e., “engine temperature … oil temperature… ambient air temperature”; see p. 3, ¶ 5; note that the VHUMS information is, impliedly or obviously, real-time data for a timely analysis).
Regarding claim 17, Le further teaches:
wherein the real-time working condition data of the operating mechanical equipment comprises a rotational speed of a component of the operating mechanical equipment and/or a load on the component of the operating mechanical equipment (i.e., “engine RPM, engine temperature … vehicle speed… ambient air temperature”; see p. 3, ¶ 5).
Conclusion
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/JOHN C KUAN/Primary Examiner, Art Unit 2857