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 .
Claim Objections
Claim 11 recites the limitation “the failure prediction unit” in line 12. There is insufficient antecedent basis for this limitation in the claim. The claim uses a definite article “the”, however, the claim 11 does not recite the claim limitation of “a failure prediction unit”.
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.
Claims 1-13 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.
As to claim 1, the claim recites “A failure prediction device for industrial equipment using multiple deep learning models selectively, comprising:
a sensor unit that collects operational data detecting an operating state of the industrial equipment;
a data prediction unit that is provided with multiple deep learning-based prediction models and predicts the operational data of the industrial equipment during a second period after a first period using the operational data collected by the sensor unit during the first period;
a prediction model selection unit that compares the operational data collected by the sensor unit during the second period with the operational data predicted by the multiple prediction models and selects any one of the multiple prediction models as an operational data prediction model of the industrial equipment; and
a failure prediction unit that predicts an occurrence time of failure of the industrial equipment using the operational data predicted by the selected operational data prediction model.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed 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 (apparatus for claim 1).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type 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 that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations.
In claim 1, the steps identified in bold type are mathematical concepts, therefore, they are considered to be abstract idea.
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 claim comprises the following additional elements:
a sensor unit that collects operational data detecting an operating state of the industrial equipment; a data prediction unit that is provided with multiple deep learning-based prediction models; a prediction model selection unit, the multiple prediction models and selects any one of the multiple prediction models as an operational data prediction model of the industrial equipment; a failure prediction unit, and the selected operational data prediction model.
The additional element “a sensor unit that collects operational data detecting an operating state of the industrial equipment” represents necessary data gathering and does not integrate the limitation into a practical application. The additional elements “a data prediction unit that is provided with multiple deep learning-based prediction models”; “a prediction model selection unit, the multiple prediction models and selects any one of the multiple prediction models as an operational data prediction model of the industrial equipment”; and “a failure prediction unit, and the selected operational data prediction model” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, a sensor unit that collects operational data detecting an operating state of the industrial equipment is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, selects any one of the multiple prediction models as an operational data prediction model of the industrial equipment is disclosed by “Poornaki US 20200210824”, Abstract, [0008], [0187, [0188], [0222]; and “Achin US 20200134489”, [0006], [0034], [0035], [0046], [0056], [0132], [0144].
The claim, therefore, is not patent eligible.
Independent claim 11 recites subject matter that is similar or analogous to that of claim 1, and therefore, the claim is also patent ineligible.
With regards to the dependent claims, claims 2-10 and 12-13 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner' s Note
Regarding Claims 1-13, the most pertinent prior arts are “Seo KR 20210141087A”, “Poornaki US 20200210824”, “Achin US 20200134489”, “Kadano US 6772038B2”, “Garvey US 20120089336”, “Heron US 20130158892”, and “Cai US 20130306216”.
As to claims 1 and 11, Seo teaches a sensor unit that collects operational data detecting an operating state of the industrial equipment (Seo, Abstract and [0036] disclose data collection unit collecting operation data of the machine tool; and the operation data refers to the spindle torque data of a machine tool, any state information generated by the machine tool, such as the error rate of a workpiece, motor operation signal, machine tool operation time, noise, and vibration, which can be collected while the machine tool is operating);
extracting features of operational data from a data preprocessing unit for the operational data collected (Seo, [0006], [0009], [0011], [0016], [0036], [0037]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “a data prediction unit that is provided with multiple deep learning-based prediction models and predicts the operational data of the industrial equipment during a second period after a first period using the operational data collected by the sensor unit during the first period”;
“a prediction model selection unit that compares the operational data collected by the sensor unit during the second period with the operational data predicted by the multiple prediction models and selects any one of the multiple prediction models as an operational data prediction model of the industrial equipment” including all limitations as claimed.
Dependent claims 2-10 and 12-13 are also distinguished over the prior art for at least the same reason as claims 1 and 11.
Examiner notes, however, that claims 1-13 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Madhavan US 20100174515” teaches “A novel procedure is disclosed, that can be incorporated into Finite Element Analysis (FEA) or other similar analysis techniques, to obtain the steady state temperature distribution in a coupled transient heat transfer analysis rapidly as well as accurately. A scale factor is used to reduce the thermal inertia per unit volume (specific heat capacity) in regions of steady state temperature distribution, thereby hastening the achievement of steady state. An application of this procedure to estimate steady state temperature distributions within cutting tools, and the estimation of cutting tool wear based on the obtained steady state temperature distributions is shown as an example.”
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/LAL CE MANG/Primary Examiner, Art Unit 2857
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