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 .
DETAILED ACTION
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 1-4, 6-11, and 13-14 are 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.
In Claim 1 (and similarly, Claim 8), the limitation “step of updating, by the machine-learning analysis database, stored vibration data based on the vibration data provided to the machine-learning analysis database” is indefinite as it is unclear what feature “stored” relates to.
For the purpose of a compact prosecution, the Examiner treated this limitation as step of updating, by the machine-learning analysis database, the acquired vibration data
based on the vibration data provided to the machine-learning analysis database.
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 1-4, 6-11, and 13-14 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 1 recites:
“A vibration measurement error determination method comprising: measuring, by a vibration sensor coupled to a structure, vibration data generated by a rotating device of the structure; a first determination step for determining, on the basis of a preset error data selection rule, whether the vibration data includes a measurement error; a second determination step for using a machine-learning algorithm to determine whether the vibration data includes a measurement error; and a final determination step for determining, on the basis of the results determined in the first determination step and the second determination step, whether the vibration data includes a measurement error, a step of providing, when the vibration data is determined in the final determination step not to include a measurement error, the vibration data to a machine-learning analysis database; and a step of updating, by the machine-learning analysis database, stored vibration data based on the vibration data provided to the machine-learning analysis database, wherein the machine-
learning algorithm extracts a plurality of sample data from the vibration data, extracts candidate feature information for determining the measurement error from the vibration data and the extracted plurality of sample data, and determines whether the vibration data includes the measurement error based on feature information extracted from the candidate feature information, and wherein the candidate feature information is at least one of: a ratio (s mean/s rms) of the mean (s mean) of the sample data to the RMS (s rms) of the sample data, a ratio (s std/d rms) of the standard deviation (s std) of the sample data to the RMS (d rms) of the vibration data, a ratio (s std/d std) of the standard deviation (s std) of the sample data to the standard deviation (d std) of the vibration data, a ratio (s mean/d var) of the mean (s mean) of the sample data to the variance (d var) of the vibration data, a ratio (s mean/d std) of the mean (s mean) of the sample data to the standard deviation (d std) of the vibration data, a ratio (s mean/d rms) of the mean (s mean) of the sample data to the RMS (d rms) of vibration data, a ratio (d mean/s rms) of the mean (d mean) of the vibration data to the RMS (s rms) of the sample data, a ratio (s std/s rms) of the standard deviation (s std) of the sample data to the RMS (s rms) of the sample data, a ratio (s var/d var) of the variance (s var) of the sample data to the variance (d var) of the vibration data, a ratio (s std/s var) of the standard deviation (s std) of the sample data to the variance (s var) of the sample data, a ratio (s var/s std) of the variance (s var) of the sample data to the standard deviation (s std) of the sample data, and a ratio (s var/s rms) of the variance (s var) of the sample data to the RMS (s rms) of the sample data.
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 groupings of subject matter 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 “using a machine-learning algorithm to determine whether the vibration data includes a measurement error” and “extracts a plurality of sample data from the vibration data, extracts candidate feature information for determining the measurement error from the vibration data and the extracted plurality of sample data, and determines whether the vibration data includes the measurement error based on feature information extracted from the candidate feature information, and wherein the
candidate feature information is at least one of …” are treated as belonging to the mathematical concepts grouping while the steps of “determining, on the basis of a preset error data selection rule, whether the vibration data includes a measurement error”, “determining, on the basis of the results determined in the first determination step and the second determination step, whether the vibration data includes a measurement error”, “providing, when the vibration data is determined in the final determination step not to include a measurement error, the vibration data to a machine-learning analysis database” are treated as belonging to mental process grouping. These mental steps represent a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. In the context of this claim, the former mental step encompasses a user comparing the error with a known rule/threshold (“observation/evaluation”) to develop a judgement whether the vibration data is data generated by a measurement error and the latter mental step corresponds to a judgement of an error based on two determination results. The new limitation related to providing the data to machine-learning database corresponds to evaluation/judgement decision-mental step. The error judgement step, under the BRI, alternatively/additionally is treated as mathematical relationship step (MPEP 2106.04.II: “construing the claims in accordance with their broadest reasonable interpretation”).
Similar limitations comprise the abstract ideas of Claim 8.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A vibration measurement error determination method comprising: measuring, by a vibration sensor coupled to a structure, vibration data generated by a rotating device of the structure; a step of updating, by the machine-learning analysis database, stored vibration data based on the vibration data provided to the machine-learning analysis database;
In Claim 8: A vibration measurement error discernment system comprising: at least one processor configured to: measure, by a vibration sensor coupled to a structure, vibration data generated by a rotating device of the structure; update stored vibration data based on the received vibration data.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a vibration data measurement step using a vibration sensor coupled to a structure and/or a processor configured to measure vibration data by measuring vibration generated in a structure generically recite collecting sample vibration data including erroneous data represent insignificant extra-solution activity of mere data gathering. Features such as a vibration sensor coupled to a structure, vibration data generated by a rotating device of the structure represent additional elements that are only tangentially relate to the judicial exception (MPEP 2106.05(g)) and also represent insignificant extra-solution activity. The steps related to “updating stored vibration data based on the vibration data” (Claim 8, and similarly in Claim 1) correspond to insignificant extra-solution activity as needed steps for the judicial exception. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a
necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does 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) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including Su and Shibuya.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-4, 6, 7, 9-11, and 13-14 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional elements in Claims 3-4 and 10-11 (using specific thresholds in abstract idea decision making) are all recited in generality and not meaningful to indicate a practical application and/or qualify for significantly more.
Examiner Note with Regards to Prior Art of Record
Claims 1-4, 6-11, and 13-14 are distinguished over prior art of record based on the reasons below.
However, in regards to Claims 1 and 8, the claims differ from the closest prior art of record, Su, Shibuya, and Morey, because the references do not disclose wherein the machine-learning algorithm extracts candidate feature information for determining the measurement error from the vibration data and the extracted plurality of sample data, and determines whether the vibration data is the data generated by the measurement error based on feature information extracted from the candidate feature information, including the specific candidate feature information as claimed, in combination with all other limitations in the claim as claimed and defined by applicant.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks, filed 9/23/2026, with respect to Claim 1 have been fully considered and are persuasive. The Final rejection of 7/29/2026 has been withdrawn.
The Examiner expresses a sincere apology for inadvertently including in the previous response a wrong copy of an office action while the office action was correctly composed on 7/29/2026. The current office action is in response to Applicant’s Response After Final Action dated 9/23/2026. The current Response to Arguments addresses the Applicant’s arguments dated 7/13/2026 and 9/23/2026.
35 U.S.C. 101
Applicant's arguments filed 7/13/2026 and 9/23/2026 have been fully considered but they are not persuasive.
Applicant's arguments filed 7/13/2026
The Applicant argues (p.8): Amended claim 1 represents such an
improvement. The specific steps of amended claim 1, as well as the specific candidate feature information listed in the amendments, lead to substantial improvements in vibration detection technology.
In Ex parte Desjardins (Sept. 26, 2025, precedential), the director explained that the improvements to technology can be improvements to elements of the
claim which may be implemented in software. In Desjardins, those improvements were to the claimed machine learning model (a software model), and not the computer that was recited in the claims. See Desjardins at 8.
Accordingly, even though elements of amended claim 1 relate to a machine-learning algorithm, applicants respectfully submit that such elements are nonetheless directed to patentable subject matter as an improvement to technology.
The Examiner respectfully disagrees.
The Examiner submits that the argument about instant application’s improvement does not correspond to recognized improvement/ practical application in Desjardins.
There, the claim was directed to training a machine learning model to learn new tasks. The USPTO long recognized that training a machine learning model (“neural network”) is not an abstract idea (see Example 39).
In the instant application, the amended features are abstract idea steps. The improvement in the abstract idea is not a qualified improvement to demonstrate a practical application/eligibility. According to the October 2019 Update on 2019 PEG: “it is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology. Further, in Synopsys: “a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty”.
Applicant's arguments filed 9/23/2026
The Applicant argues (p.7): First, claim 1 has been amended to recite, inter alia, "measuring, by a vibration sensor coupled to a structure, vibration data generated by a rotating device of the structure " This is a definite and specific step that involves a physical measurement performed by a physical structure (a vibration sensor) on another physical object (structure, e.g., a nuclear power plant, see [0003]).
The Examiner respectfully disagrees. According to MPEP 2106.04(d): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two.”
The Applicant argues (p.8): In addition to the measurement step of amended claim 1, providing vibration data to a machine-learning analysis database, and updating, by the machine-learning analysis database, stored vibration data based on the vibration data provided to the machine-learning analysis database, are not mental steps that can be performed by a human mind, or a mathematical process. Rather, these steps, along with the specific candidate feature information listed in the amended claim, represent an improvement to vibration measurement technology that cannot be found in the prior art ... The applicants of the present application have found that the feature information of amended claim 1 enables the selection of incorrectly measured vibration data, thereby improving the reliability of the vibration data used as the basis for predictive diagnostics. … This noise can result in significant measurement errors, which is especially important in sensitive installations such as nuclear power plants. Therefore, it is important to be able to accurately identify measurement errors to accurately gauge the magnitude of actual vibrations so that accurate vibration measurements can be performed.
The Examiner disagrees. The measurement technology is not improved as no meaningful additional elements/significantly more are recited. The improvement relates to the abstract idea which is not a qualified improvement.
MPEP 2106.05(a).II: “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology … the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology”.
With regards to the “mental steps” argument, the Examiner addressed relevant steps in the rejection that a human mind is capable of performing.
The Applicant argues (p.9): The specific steps of claim 1, as well as the specific candidate feature information listed in the amendments, lead to substantial improvements in vibration detection technology. In Ex parte Desjardins (Sept. 26, 2025, precedential), the Director explained that the improvements to technology can be improvements to elements of the claim which may be implemented in software. In Desjardins, those improvements were to the claimed machine learning model (a software model), and not the computer that was recited in the claims. See Desjardins at 8. The present claims reflect such an improvement, both to a method for determining measurement error as well as to improvements to a machine learning algorithm used in the method for determining measurement error.
The Examiner responded to a similar argument presented in the previous Arguments/Remarks by the Applicant and incorporates here the previous Response (see above).
35 U.S.C. 103
Applicant’s arguments, see Applicant Arguments/Remarks, filed 7/13/2026 and 9/23/2026, with respect to Claim 1 have been fully considered and are persuasive in view of the amendments. The 35 U.S.C. 103 rejection of Claim 1 (8) has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stewart V. Bowers et al. (US 20180217109) discloses detecting faulty collection of vibration data including detecting and discarding undesirable vibration data prior to analysis of the data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (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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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
