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 .
Status of the claims
The amendment received on November 7, 2025 has been acknowledged and entered. Claims 1-3 and 5-6 are amended. Claims 4 and 7 are cancelled. Thus, claims 1-3 and 5-6 are currently pending.
Response to Arguments
Applicant’s arguments filed November 7, 2025 with respect to the claim rejection of claims 1-3 and 5-6 under 35 U.S.C. 112(a) have been fully considered and are persuasive. Thus, the claim rejection of claims 1-3 and 5-6 under 35 U.S.C. 112(a) has been withdrawn.
Applicant’s arguments filed on April 21, 2025 with respect to claims 1-3 and 5-6 under 35 U.S.C. 101 have been considered but are moot because the new ground of rejection.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, line 52, a term, “corrected amplitude amplitude” should read “corrected amplitude”.
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 1-3 and 5-6 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:
An online testing and diagnosis method for vibration characteristics of wind turbine blades, wherein steps of testing and diagnosing blade vibration are as follows:
S1: installing vibration sensors and environmental parameter sensors at selected positions of a blade, and automatically determining an optimized sampling rate according to a vibration amplitude monitored by the vibration sensors in a real time and according to environmental parameter changes monitored by the environmental parameter sensors in a real time; wherein the automatically determining comprises taking a larger value between a sampling rate corresponding to the vibration amplitude and a sampling rate corresponding to the environmental parameter changes as the optimized sampling rate, or taking an average value of the sampling rate corresponding to the vibration amplitude and the sampling rate corresponding to the environmental parameter changes as the optimized sampling rate;
S2: extracting selected features reflecting health status of the blade from the vibration amplitude and environmental parameter changes to evaluate an impact of wind speed, temperature, and environmental factors on vibration characteristics;
S3: constructing an one-dimensional convolutional neural network model for performing a time sequence analysis, extracting time sequence data and at least one vibration signal from the vibration amplitude and environmental parameter changes by using the one-dimensional convolutional neural network model to identify a type of damage among different types of damages comprising erosion, crack, and impact, and evaluate a damage degree; wherein a convolutional layer of the one- dimensional convolutional neural network model is represented as: y=f(b+W*x); wherein f is an activation function, b is a bias term, W is a weight of convolutional kernel, and x is an inputting signal; and wherein a periodic feature, a trending feature, and an instantaneous feature of the time sequence data are extracted by utilizing a time sequence analysis; and
S4: automatically adjusting a warning threshold based on the vibration amplitude and environmental changes monitored in a real time and a historical trend of the vibration amplitude and environmental changes, and drafting a preventive maintenance plan based on analysis of predicted damage type and vibration mode;
wherein S2 comprises: inputting a measured value of a vibration amplitude and a measured value of an environmental parameter change into a compensation model to calculate an expected "environmental impact vibration feature" under current environmental condition to obtain a corrected vibration amplitude, wherein a main influence of wind speed on vibration of the blade is approximated as a linear relationship, and the compensation model is expressed as:
V
c
o
r
r
w
i
n
d
(
f
)
=
k
v
×
V
a
c
t
u
a
l
wherein,
V
c
o
r
r
w
i
n
d
(
f
)
is a corrected wind speed under an effect of the wind speed obtained by the compensation model, kv is an effect coefficient of the wind speed, and Vactual is a measured wind speed;
wherein the corrected vibration amplitude is obtained by subtracting the calculated "environmental impact vibration feature" from an original vibration amplitude, and the corrected vibration amplitude is performed with an in-depth analysis to evaluate a health status of the blade;
wherein the corrected amplitude is expressed as :
Acorrected (f)=Araw(f) – Vcorr wind (f) -Tcorr (f)
wherein:
Acorrected(f): represents a corrected vibration amplitude under frequency f;
Araw(f): represents a vibration amplitude under frequency f obtained by an original measure;
Vcorrwind( f): represents a corrected wind speed obtained by the compensation model;
Tcorr(f) represents a corrected temperature obtained by the compensation model;
Wherein a prediction model is established based on the corrected vibration amplitude under frequency f;
and wherein in S4, a trend of vibration feature over time is analyzed based on historical vibration data and known damage events, vibration feature patterns under different types of damage are identified, a dynamic threshold model is set, and a warning threshold is dynamically adjusted according to a real-time data and a prediction model output;
wherein the warning threshold is set as one standard deviation of a normal vibration feature prediction interval, and a health status and potential risks of the blade are evaluated according to a deviation degree between a damage prediction result and a vibration feature;
wherein different maintenance trigger thresholds are set according to a risk level.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: 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).
Step 2A, Prong One: 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 when recited as such in a claim limitation that 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.
For example, the additional elements of “automatically determining an optimized sampling rate according to a vibration amplitude monitored by the vibration sensors in a real time and according to environmental parameter changes monitored by the environmental parameter sensors in a real time; wherein the automatically determining comprises taking a larger value between a sampling rate corresponding to the vibration amplitude and a sampling rate corresponding to the environmental parameter changes as the optimized sampling rate, or taking an average value of the sampling rate corresponding to the vibration amplitude and the sampling rate corresponding to the environmental parameter changes as the optimized sampling rate (see paras. [0015]-[0023] of instant application), S2: extracting selected features reflecting health status of the blade from the vibration amplitude and environmental parameter changes to evaluate an impact of wind speed, temperature, and environmental factors on vibration characteristics (see para. [0045] of instant application), S3: constructing an one-dimensional convolutional neural network model for performing a time sequence analysis, extracting time sequence data and at least one vibration signal from the vibration amplitude and environmental parameter changes by using the one-dimensional convolutional neural network model to identify a type of damage among different types of damages comprising erosion, crack, and impact, and evaluate a damage degree; wherein a convolutional layer of the one- dimensional convolutional neural network model is represented as: y=f(b+W*x); wherein f is an activation function, b is a bias term, W is a weight of convolutional kernel, and x is an inputting signal; and wherein a periodic feature, a trending feature, and an instantaneous feature of the time sequence data are extracted by utilizing a time sequence analysis (see paras. [0091]-[0094] of instant application), S4: automatically adjusting a warning threshold based on the vibration amplitude and environmental changes monitored in a real time and a historical trend of the vibration amplitude and environmental changes, and drafting a preventive maintenance plan based on analysis of predicted damage type and vibration mode (see para. [0039] of instant application); wherein S2 comprises: inputting a measured value of a vibration amplitude and a measured value of an environmental parameter change into a compensation model to calculate an expected "environmental impact vibration feature" under current environmental condition to obtain a corrected vibration amplitude, wherein a main influence of wind speed on vibration of the blade is approximated as a linear relationship, and the compensation model is expressed as:
V
c
o
r
r
w
i
n
d
(
f
)
=
k
v
×
V
a
c
t
u
a
l
wherein,
V
c
o
r
r
w
i
n
d
(
f
)
is a corrected wind speed under an effect of the wind speed obtained by the compensation model, kv is an effect coefficient of the wind speed, and Vactual is a measured wind speed; wherein the corrected vibration amplitude is obtained by subtracting the calculated "environmental impact vibration feature" from an original vibration amplitude, and the corrected vibration amplitude is performed with an in-depth analysis to evaluate a health status of the blade; wherein the corrected amplitude is expressed as :
Acorrected (f)=Araw(f) – Vcorr wind (f) -Tcorr (f)
wherein:
Acorrected(f): represents a corrected vibration amplitude under frequency f;
Araw(f): represents a vibration amplitude under frequency f obtained by an original measure;
Vcorrwind( f): represents a corrected wind speed obtained by the compensation model;
Tcorr(f) represents a corrected temperature obtained by the compensation model;
Wherein a prediction model is established based on the corrected vibration amplitude under frequency f (see paras. [0064]-[0074] of instant application); and wherein in S4, a trend of vibration feature over time is analyzed based on historical vibration data and known damage events, vibration feature patterns under different types of damage are identified, a dynamic threshold model is set, and a warning threshold is dynamically adjusted according to a real-time data and a prediction model output (see para. [0090] of instant application); wherein the warning threshold is set as one standard deviation of a normal vibration feature prediction interval, and a health status and potential risks of the blade are evaluated according to a deviation degree between a damage prediction result and a vibration feature (see para. [0090] of instant application); wherein different maintenance trigger thresholds are set according to a risk level (see para. [0090] of instant application)” are mathematical calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical concepts, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. This judicial exception is not 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. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: an online testing and diagnosis method for vibration characteristics of wind turbine blades (preamble); installing vibration sensors and environmental parameter sensors at selected positions of a blade.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Jiang (CN111649887A), Liu (CN 104515677A), Luo (CN 109724652 A), Xiong (EP2258942A2), Ebert (EP2565444B1)). For example, Jiang, Ebert, Liu, and Luo teach the limitation of an online testing and diagnosis method for vibration characteristics of wind turbine blades, (see page 2, lines 29-32 and page 5, line 21 of Jiang; page 5, lines 35-37 of Ebert; page 5, lines 21 and page 2, line 29 of Liu; page 5, lines 32-34 and page 5, lines 35-37 of Luo). Further, Xiong and Ebert teach the limitation of installing vibration sensors and environmental parameter sensors at selected positions of a blade (para. [0018] of Xiong; page 7, lines 32-34 of Ebert). The independent claim, therefore, is not patent eligible.
Regarding claim 2,
The additional element of “in S1, key positions of the blade that are most prone to damage, such as root, tip, middle, and known weak points of the blade, are determined; different types of vibration sensors and environmental parameter sensors are installed at these key positions, and an algorithm is designed to dynamically adjust the sampling rate based on a vibration amplitude threshold and environmental parameter changes” is well-understood, routine, and conventional in the relevant based on the prior art of record (page 5, lines 32-34 and page 9, lines 15-17 of Luo; page 8, lines 20-24 of Rao (CN102080625 A); paras. [0016] and [0060] of Kramer (US 2015/0345467 A1)).
Regarding claims 3 and 5-6,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations. The explanation for the rejection of Claim 1 therefore is incorporated herein and applied to Claims 3 and 5-6. These claims therefore stand rejected for similar reasons as explained in above Claim 1.
No prior art is being applied to Claim 1 because the prior art does not disclose or make obvious “S2: extracting selected features reflecting health status of the blade from the vibration amplitude and environmental parameter changes to evaluate an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: constructing an one-dimensional convolutional neural network model for performing a time sequence analysis, extracting time sequence data and at least one vibration signal from the vibration amplitude and environmental parameter changes by using the one-dimensional convolutional neural network model to identify a type of damage among different types of damages comprising erosion, crack, and impact, and evaluate a damage degree; wherein a convolutional layer of the one- dimensional convolutional neural network model is represented as: y=f(b+W*x); wherein f is an activation function, b is a bias term, W is a weight of convolutional kernel, and x is an inputting signal; and wherein a periodic feature, a trending feature, and an instantaneous feature of the time sequence data are extracted by utilizing a time sequence analysis; and S4: automatically adjusting a warning threshold based on the vibration amplitude and environmental changes monitored in a real time and a historical trend of the vibration amplitude and environmental changes, and drafting a preventive maintenance plan based on analysis of predicted damage type and vibration mode; wherein S2 comprises: inputting a measured value of a vibration amplitude and a measured value of an environmental parameter change into a compensation model to calculate an expected "environmental impact vibration feature" under current environmental condition to obtain a corrected vibration amplitude, wherein a main influence of wind speed on vibration of the blade is approximated as a linear relationship, and the compensation model is expressed as:
V
c
o
r
r
w
i
n
d
(
f
)
=
k
v
×
V
a
c
t
u
a
l
wherein,
V
c
o
r
r
w
i
n
d
(
f
)
is a corrected wind speed under an effect of the wind speed obtained by the compensation model, kv is an effect coefficient of the wind speed, and Vactual is a measured wind speed; wherein the corrected vibration amplitude is obtained by subtracting the calculated "environmental impact vibration feature" from an original vibration amplitude, and the corrected vibration amplitude is performed with an in-depth analysis to evaluate a health status of the blade; wherein the corrected amplitude is expressed as:
Acorrected (f)=Araw(f) – Vcorr wind (f) -Tcorr (f)
wherein:
Acorrected(f): represents a corrected vibration amplitude under frequency f;
Araw(f): represents a vibration amplitude under frequency f obtained by an original measure; Vcorrwind( f): represents a corrected wind speed obtained by the compensation model; Tcorr(f) represents a corrected temperature obtained by the compensation model; wherein a prediction model is established based on the corrected vibration amplitude under frequency f; and wherein in S4, a trend of vibration feature over time is analyzed based on historical vibration data and known damage events, vibration feature patterns under different types of damage are identified, a dynamic threshold model is set, and a warning threshold is dynamically adjusted according to a real-time data and a prediction model output; wherein the warning threshold is set as one standard deviation of a normal vibration feature prediction interval, and a health status and potential risks of the blade are evaluated according to a deviation degree between a damage prediction result and a vibration feature; wherein different maintenance trigger thresholds are set according to a risk level.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANGKYUNG LEE whose telephone number is (571)272-3669. The examiner can normally be reached on Monday-Friday 8:30am-4:00pm.
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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