DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. Claim 1-2 are objected to because of the following informalities: a) In claim 1 line 5-6, please change “and a D-S evidence theory to obtain oil fusion data;” to:
--and a Dempster-Shafer (D-S) evidence theory to obtain oil fusion data;--. b) In claim 2 line 2-3, please change “a spectral analysis unit for obtaining the spectral data of the oil based on the spectral analyzer” to:
--a spectral analysis unit for obtaining the spectral data of the oil based on a spectral analyzer--. Appropriate correction is required.
Claim Rejections - 35 USC § 112
3. 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.
Claim 7 is 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.
a) Claim 7 lines 5-7 recites “when an error is greater than a preset threshold, the oil prediction model is continuously trained, and a loop of is repeated until the error meets the preset threshold...”. The claim appears to be missing language after “a loop of...”, as it is not clear what action or step is repeated in a loop until the error meets the preset threshold. Therefore the claim appears to be missing critical information to particularly point out or claim the subject matter which the inventor regards as the invention.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 and 5-7 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.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-7 are to a machine. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts and/or mental processes:
1. A big data analysis system for engine quality detection and prediction, comprising: an oil acquisition module for collecting oil in an engine; an oil analysis module connected with the oil acquisition module for obtaining spectral data, ferrographic data, and physicochemical data of the oil; a data fusion module connected with the oil analysis module for fusing the spectral data, ferrographic data, and physicochemical data based on a fuzzy logic and a D-S evidence theory to obtain oil fusion data; an oil prediction module connected with the data fusion module for constructing an oil prediction model, training the oil prediction model based on the oil fusion data, and predicting the oil in the engine based on a trained oil prediction model to obtain oil prediction data; a quality detection module connected with the oil prediction module for obtaining a wear degree of the engine and completing a quality prediction of the engine based on the oil prediction data.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the limitations “for fusing the spectral data, ferrographic data, and physicochemical data based on a fuzzy logic and a D-S evidence theory to obtain oil fusion data” are considered as words serving the same purpose as a formula to describe the mathematical derivation of oil fusion data. The limitations “for constructing an oil prediction model” describes a mental process to evaluate data to form data relationships to generate a predicted analysis result regarding oil, or amount to a mathematical concept if the oil prediction model is a mathematically-based formula. The limitations of “training the oil prediction model,” when given its broadest reasonable interpretation in light of the background, amount to carrying out mathematical calculations to generate a trained prediction model. Paragraph [0049] in the background specification as originally filed describes the training process by using an “error back propagation algorithm,” which supports the plain meaning of the claim limitation terms as mathematically-based calculations to generate a trained oil prediction model (see similar Example 47 claim 2 from the July 2024 Subject Matter Eligibility Examples from the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 FR 58128). Patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101 (see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)). The limitations of “predicting the oil in the engine based on a trained prediction model to obtain prediction data” amount to the mathematical action of using a trained mathematical model which takes input parameters and returns output parameters. The limitations of “for obtaining a wear degree of the engine and completing a quality prediction of the engine based on the oil prediction data” amount to a mental process to form a judgment of a wear degree and quality prediction based on analyzing the oil prediction data, or a mathematical-based calculation to derive a numerical wear degree and quality rating based on the inputs of the oil prediction data.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the recitations for “a big data analysis system for engine quality detection and prediction comprising:,” “an oil analysis module connected with the oil acquisition module...,” “a data fusion module connected with the oil analysis module...,” “an oil prediction module connected with the data fusion module...”, and “a quality detection module connected with the oil prediction module” amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f), see also specification as originally filed paragraph [0029] which describes the modules as computer-executable instructions); and (2) the recitations for “an oil acquisition module for collecting oil in an engine” and “obtaining spectral data, ferrographic data, and physicochemical data of the oil” amount to to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)) to collect the necessary input data.
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception.
The recitations for “a big data analysis system for engine quality detection and prediction comprising:,” “an oil analysis module connected with the oil acquisition module...,” “a data fusion module connected with the oil analysis module...,” “an oil prediction module connected with the data fusion module...”, and “a quality detection module connected with the oil prediction module” do not add significantly more because they describe amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f), see also specification as originally filed paragraph [0029]). The use of generic computer equipment is considered insignificant additional elements. As recited in the MPEP, 2106.07(b), merely adding a generic computer, generic computer components, or a programmed computer (including a series of programmed modules) to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94). The recitations for “an oil acquisition module for collecting oil in an engine” and “obtaining spectral data, ferrographic data, and physicochemical data of the oil” do not add something significantly more because such limitations amount to to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and do not describe any gathering of data in an unconventional way.
Dependent claims 2 and 5-7 contain additional limitations that fall under the abstract idea groupings of a mental process and/or mathematical concepts to describe further data processing/analysis steps to generate additional additional data variables and values. Dependent claim 3 contains patent-eligible subject matter because it describes where “the wear particle image acquisition sub-unit is used for constructing a wear particle detection model based on a U-net network to obtain a ferrographic image, and then training the wear particle detection model based on the ferrographic image,” which cannot be performed mentally or on pen and paper by a person, where the generation of a ferrographic image and training based on the image is necessarily rooted in technology and extends beyond generally linking to a technological environment. Dependent claim 4 depends from claim 3 and contains patent-eligible subject matter for at least the same reasons as given for claim 3.
5. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the calculated wear degree and quality prediction to drive a transformation, change in physical operation, or repair/maintenance of a technology or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Allowable Subject Matter
6. Claims 1-2 and 5-6 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. Claim 7 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and 35 U.S.C. 112(b), set forth in this Office action. Claims 3-4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
7. The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 1, the closest prior art, Potyrailo et al. (US Pat. Pub. 2019/0156600, hereinafter “Potyrailo”) at least teaches a big data analysis system for engine quality detection and prediction (Potyrailo abstract teaches a locomotive sensor system for determining an unhealthy state (quality state) of an engine, and paragraph [0160] teaches where the system is also equipped to predict the remaining life of the engine in which oil is disposed), comprising: an oil acquisition module for collecting oil in an engine (Potyrailo paragraph [0144] teaches a fluid reservoir as an oil acquisition module for holding and collecting oil in an engine); an oil analysis module connected with the oil acquisition module for obtaining spectral data (Potyrailo paragraph [0144] teaches one or more sensors as an oil analysis module disposed within (i.e., connected with) the fluid reservoir for obtaining measured sensor data, and paragraph [0147] teaches where the sensor may detect characteristics or properties as a resonant impedance spectral response).8. However, claim 1 contains allowable subject matter because the closest prior art, Potyrailo et al. (US Pat. Pub. 2019/0156600) fails to anticipate or render obvious a big data analysis system comprising obtaining ferrographic data, and physicochemical data of the oil; a data fusion module connected with the oil analysis module for fusing the spectral data, ferrographic data, and physicochemical data based on a fuzzy logic and a D-S evidence theory to obtain oil fusion data; an oil prediction module connected with the data fusion module for constructing an oil prediction model, training the oil prediction model based on the oil fusion data, in combination with the rest of the claim limitations as claimed and defined by the Applicant. There is no recitation or suggestion for obtaining ferrographic and physicochemical data, and performing fusion of such data with spectral data using fuzzy logic and D-S evidence theory as described in the claim.
9. Dependent claims 2-7 depend from claim 1 and contain allowable subject matter for at least the same reasons as given for claim 1.
Pertinent Art
10. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
B. Xu et al. (US Pat. No. 11,694,101) discloses a Predictive Sensor System for Aircraft Engines with Graphical User Interface. C. Brook (US Pat. No. 11,694,116) discloses Vehicle Resiliency, Driving Feedback and Risk Assessment Using Machine Learning-Based Vehicle Wear Scoring.
D. Kinard (US Pat. Pub. 2018/0017541) discloses Systems and Methods to Detect and Measure Materials in Oil.
E. Sjogren et al. (Us Pat. Pub. 2021/0334656) discloses Computer-Implemented Method, Computer Program Product and System for Anomaly Detection and/or Predictive Maintenance.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 9/16/2026
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