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 . Claims 1-10 are pending.
Drawings
The drawings are objected to because FIG. 2 appears to be an informal screenshot featuring white font on a gray background, which isn’t legible. The drawings are required to feature high contrast, black-and-white figures that are reproducible.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1-10 are objected to because of the following informalities.
In claim 1, applicant recites “a deep learning system for automatically learn and extract features from raw DFOS sensor data,” which should read: “a deep learning system for automatically learning and extracting features from raw DFOS sensor data,” for consistency.
In claim 1, applicant recites “wherein the DL-LM-DFOS enhanced system is configured to detect maintenance issues from the fiber optic sensing (DFOS) sensor data and the textual data,” which should read: “wherein the DL-LM-DFOS enhanced system is configured to detect maintenance issues from the DFOS sensor data and the textual data,” for consistency.
In claim 5, applicant recites “the DL-LM-DFOS system is configured to report detected maintenance issues to appropriate maintenance personnel,” which should read: “the DL-LM-DFOS enhanced system is configured to report detected maintenance issues to appropriate maintenance personnel,” for consistency.
The dependent claims are objected to by virtue of their dependency. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 4-10 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.
Claim 4 recites the limitations “an optical fiber method of claim 1” and “the resources.” There is insufficient antecedent basis for these limitations in the claim. Applicant’s claim 1 refers to a system, not a method, and there is no mention of “resources.” Given this ambiguity, the metes and bounds are indeterminate, and the claim is rejected for indefiniteness.
The dependent claims are rejected by virtue of their dependency. 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a system (i.e., a machine), thereby passing Step 1. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 is:
capturing high-dimensional data about strain, temperature, or vibration of the wind turbines;
automatically [learning] and [extracting] features from raw DFOS sensor data; and
processing and interpreting textual data associated with the DFOS sensor data;
[detecting] maintenance issues from the fiber optic sensing (DFOS) sensor data and the textual data.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, 1) obtaining data describing strain, temperature, or vibration of wind turbines; 2) extracting and learning (i.e., recording) features from raw sensor data; 3) processing and interpreting textual data associated with the sensor data; and 4) detecting maintenance issues associated with the sensor data and the textual data. These are steps that an administrator could perform manually, given a dataset and based on their knowledge. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to discerning maintenance issues, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0004] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: a distributed fiber optic sensing (DFOS) system; a deep learning system for; a large language model for; wherein the DL-LM-DFOS enhanced system is configured to.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0021], [0051], and [0077]-[0081] of applicant’s specification as filed, for example.
Examiner interprets “a deep learning system for,” described in [0051] of applicant’s specification as filed, as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, “a deep learning system for” is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claim 2, in addition to narrowing the abstract idea above with additional steps, recites further additional elements: wherein the deep learning system comprises a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to. Similar to above, these additional elements are recited in a results-oriented manner and merely being used to facilitate the tasks of the narrowed abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f). Whether viewed alone or in combination, this does not integrate the abstract idea into practical application and/or add significantly more.
Dependent claims 3-10 further narrow the abstract idea above with additional abstract steps and/or information. This narrowing of the abstract idea does not integrate the abstract idea into practical and/or add significantly more.
Accordingly, claims 1-10 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (KR 20210015299 A; page numbers below correspond to those of attached translation) in view of Kim (KR 20230055781 A; page numbers below correspond to those of attached translation) and Poupyrev (US 20250068885; provisional 63/578,460, with supporting disclosure, filed 8/24/23).
Claim 1
Choi discloses:
A deep learning enhanced system for wind turbines {1 is a conceptual diagram illustrating a system for analyzing an operating state of a device according to an embodiment of the present invention. Page 3.
That is, the sensor 52 may be attached to any part wherever the vibration of the wind turbine 50 is measured. Page 3.
The artificial neural network may include any one of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). In addition, the artificial neural network may be generated by combining at least one of CNN, RNN, and LSTM. In addition, the learning unit 400 may apply various deep learning algorithms in addition to the aforementioned artificial neural network. Pages 3-4.} comprising:
a distributed system for capturing high-dimensional data about strain, temperature, or vibration of the wind turbines {As shown in FIG. 10, in order to measure the vibration generated by the power generation of the wind power generator 50, a predetermined axial direction and a radial direction around the power generation unit 51 and the gear unit 53 of the wind power generator 50 are measured, i.e., capturing high-dimensional data about vibration of the wind turbines. The sensor 52 may be attached, as well as the sensor may be attached to the bearing 54 connecting the gear part 53 and the blade 55, i.e., a distributed system. The gear part 53 is for transmitting the rotational force of the blade 55 to the power generation part 51, and the gear part 53 has a low-speed shaft, a gear box, and a brake ( brake), etc. That is, the sensor 52 may be attached to any part wherever the vibration of the wind turbine 50 is measured. Page 3.};
a deep learning system for automatically learn and extract features from raw sensor data {The present invention relates to a system for analyzing the operating state of a device, and more particularly, extracting data sensed from the device in an image form according to frequency conversion, and inputting the extracted image to a learned artificial neural network to analyze the operating state of the device. It relates to a system for analyzing the operational state of the device that can be used. Page 1.
The artificial neural network may include any one of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). In addition, the artificial neural network may be generated by combining at least one of CNN, RNN, and LSTM. In addition, the learning unit 400 may apply various deep learning algorithms in addition to the aforementioned artificial neural network. Pages 3-4.}.
Choi doesn’t explicitly disclose, however, Kim, in a similar field of endeavor directed to predicting maintenance using deep learning, teaches:
using distributed fiber optic sensing {The vibration sensor may include a sensor for measuring vibration data including vibration displacement information and acceleration information of the guided weapon, and various types of sensors such as a piezoelectric acceleration sensor, a cantilever vibration method, and an optical fiber method. Page 4.}
fiber optic sensing (DFOS); DFOS {See previous citation to page 4.}
wherein the DL-LM-DFOS enhanced system is configured to detect maintenance issues from the fiber optic sensing (DFOS) sensor data and the textual data {In addition, the data collection unit 110 may collect maintenance data for each of a plurality of parts of the equipment. Maintenance data for each of a plurality of parts of the equipment may include at least one of real-time sensor result data of parts included in the machine, failure and maintenance history data of parts included in the machine, and systematic data of parts included in the machine. Page 3.
For example, the data collection unit 110 includes text data such as each replacement item, replacement date, replacement cycle and defect details, maintenance details of each part included in a specific guided weapon, and aging rate of parts based on the environment. Maintenance data can be collected. Page 4.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Choi to include the features of Kim. Given that Choi is directed to determining abnormalities associated with wind turbines, one of ordinary skill in the art would have been motivated to look to Kim, in order to facilitate determining an imbalance or misalignment of the equipment, in addition to determining the degree of deterioration {page 4 of Kim}.
The combination of Choi and Kim, while teaching DFOS sensor data {See previous citation to page 4 of Kim.} doesn’t explicitly teach, however, Poupyrev, in a similar field of endeavor directed to a platform for generating content based on sensor data, teaches:
language model {The server system 106 hosts an integrated multimodal neural network platform and acts as a hub to connect a plurality of sensors 102 with the one or more client devices 104. The server system 106 is configured to collect sensor data 120 from the plurality of sensors 102, implement an LLM 150 that processes the sensor data 120, and generate a user-friendly output 130 (e.g., text, video, audio, program, user interface) based on the sensor data 120. [0064].
Also see [0004]: The integrated multimodal neural network platform includes a server system configured to collect the sensor data from one or more sensors, generate one or more information items characterizing the sensor data, and apply a neural network (e.g., a deep neural network, a large language model (LLM)) to process the one or more information items and generate a neural network (NN) output (e.g., an LLM output).};
a large language model for processing and interpreting textual data associated with the sensor data {The server system 106 hosts an integrated multimodal neural network platform and acts as a hub to connect a plurality of sensors 102 with the one or more client devices 104. The server system 106 is configured to collect sensor data 120 from the plurality of sensors 102, implement an LLM 150 that processes the sensor data 120, and generate a user-friendly output 130 (e.g., text, video, audio, program, user interface) based on the sensor data 120, i.e., interpreting data. In some implementations, the server system 106 pre-processes the collected sensor data 120, e.g., using a sensor data processing model 160, before the sensor data 120 is processed by the LLM 150. In some implementations, the server system 106 is configured to execute a user application via which the sensor data 120 is processed by the LLM to generate the output 130 associated with the sensor data 120 on a server side. [0064].
Also see [0089]: Data processing model(s) 344 for processing sensor data with or without other types of data (e.g., video, image, audio, or textual data) using deep learning techniques, where in some implementations, the data processing models 344 include a sensor data processing model 160 and an LLM 150}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Choi and Kim to include the features of Poupyrev. Given that Choi is directed to determining abnormalities associated with wind turbines, one of ordinary skill in the art would have been motivated to look to Poupyrev, in order to facilitate processing multiple modalities of data (e.g., sensor data and content data) and generate multimodal outputs that are convenient for users and their client devices to percept {[0003] of Poupyrev}.
(Examiner notes that (DL-LM-DFOS) is an acronym describing a combined deep learning (DL), language model (LM), and distributed fiber optic sensing (DFOS) system, which is demonstrated above via the combination of references.)
Claim 2
Choi further discloses: wherein the deep learning system comprises a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to learn and extract the features from the raw sensor data {The artificial neural network may include any one of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). In addition, the artificial neural network may be generated by combining at least one of CNN, RNN, and LSTM. In addition, the learning unit 400 may apply various deep learning algorithms in addition to the aforementioned artificial neural network. Pages 4-5.}.
Kim further teaches: DFOS sensor data {The vibration sensor may include a sensor for measuring vibration data including vibration displacement information and acceleration information of the guided weapon, and various types of sensors such as a piezoelectric acceleration sensor, a cantilever vibration method, and an optical fiber method. Page 4.}.
The motivation and rationale to include the additional features of Kim is the same as set forth above.
Claim 3
Kim further teaches: wherein the textual data associated with the DFOS sensor data includes maintenance logs, operational notes, or alarm messages {Maintenance data for each of a plurality of parts of the equipment may include at least one of real-time sensor result data of parts included in the machine, failure and maintenance history data of parts included in the machine, and systematic data of parts included in the machine, i.e., maintenance logs and/or operational notes. Page 3.}.
The motivation and rationale to include the additional features of Kim is the same as set forth above.
Claims 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Choi, Kim, Poupyrev, further in view of Damgaard (US 20200141392).
Claim 4
The combination of Choi, Kim, and Poupyrev, while teaching the features above, doesn’t explicitly teach, however, Damgaard, in a similar field of endeavor directed to wind turbine monitoring, teaches: wherein the DFOS system includes an optical fiber method of claim 1 in which the resources allocated are selected from the group consisting of repair crews, equipment, and material {As if the case with the changing residual a failure rate may also be directly reflected in the health value. Hence, if from the database 6 it is found that a particular component is failing at high frequency after 3 years of operation, this information may also change the health value to alert e.g. service personal or control that something may soon happen to component, i.e., resources allocated from the group consisting of repair crews, equipment, and material. [0091].}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Choi, Kim, and Poupyrev to include the features of Damgaard. Given that Choi is directed to determining abnormalities associated with wind turbines, one of ordinary skill in the art would have been motivated to look to Damgaard, in order to facilitate automatically determining the wind turbine monitoring results, in addition to ensuring a uniform interpretation of said results {[0004] of Damgaard}.
Claim 5
Damgaard further teaches: wherein the DL-LM-DFOS system is configured to report detected maintenance issues to appropriate maintenance personnel {As if the case with the changing residual a failure rate may also be directly reflected in the health value. Hence, if from the database 6 it is found that a particular component is failing at high frequency after 3 years of operation, this information may also change the health value to alert e.g. service personal or control that something may soon happen to component, i.e., report detected maintenance issues to appropriate maintenance personnel. [0091].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Claim 6
Damgaard further teaches: wherein the detected maintenance issue is a generator fault or generator bearing failure {Part of the information provided may be used to divide the wind turbine into sub-functions associated with main parts for the particular sub-function to work and thereby link the faulty part hereto. An example of a sub-function could be generator including main parts such as: shaft, bearing, rotor, stator, coils, etc. Another example of a sub-function could be the electrical system including at main parts such as: switchgear, cables, contactors, relays, etc. a faulty part could e.g. be one of these main parts. [0175].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Claim 7
Damgaard further teaches: wherein the detected maintenance issue is a blade crack or icing condition {According to an embodiment of the invention, the analysis includes determining a cause of the defect of the wind turbine part as at least one of the list of failure causes comprising: environment, grid failure, lightning, wind turbine part worn, wind turbine part broken, loose parts, corrosion, erosion, missing or wrong maintenance, human error, vibrations, fatigue, pollution, wrong material, documentation error, manufacturing error, installation error, management error, external damage, disconnection, severed wire or wrong parameter settings. Environment may e.g. be high wind, icing of blades, high humidity, etc. Pollution may e.g. be oil or water splash, dust, moisture, etc. Documentation error may e.g. be missing procedures, specifications or drawings, etc. Management error may e.g. be failure regarding planning, organizing, precision maintenance, etc. [0164].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Claim 8
Damgaard further teaches: wherein the detected maintenance issue is an overheating condition {According to an embodiment of the invention, the analysis includes determine at least one abnormalities of the list of abnormalities comprising: overspeed, overload, overheating, noise, odour, smoke, discoloration, vibration, reduced power production, non-planned stop, failure to start production on demand, failure to stop production on demand, abnormal instrument reading, external pollution, internal pollution, loose items, low pressure, high pressure, low temperature and high temperature. The selected abnormality may be referred to as the failure mode of the wind turbine caused by the defect of defective part. Abnormal instrument reading could e.g. be false alarms, faulty instrument reading, etc. External pollution could e.g. be detection of leakages of hydraulic oil, lube, oil coolant, etc. Internal pollution could e.g. be detection of water, moisture, dirt within e.g. a panel located in the wind turbine. [0163].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Claim 9
Damgaard further teaches: wherein the detected maintenance issue is a corrosion issue {See previous citation to [0164].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Claim 10
Damgaard further teaches: wherein the detected maintenance issue is a cable failure due to movement, temperature, or load outside operational design parameters {In embodiments of the invention the residual 8 may be compared to a parameter or component defined threshold value such as e.g. a predefined temperature level. In an example if the difference between the measured component value 3 and the estimated component value 6 exceeds a parameter defined threshold of e.g. 10° C. an alarm is set, i.e., wherein the detected maintenance issue is a cable failure due to movement, temperature, or load outside operational design parameters. When an alarm is active it indicates a problem which has to be further investigated. In case the problem requires e.g. a shutdown of the wind turbine 2 it might be advantageous to add a parameter defined threshold which when exceeded sets a warning e.g. at 5° C. in order for the wind turbine controller 10 to take actions preventing the residual 8 to increase further thereby preventing the shutdown. [0138].}.
The motivation and rationale to include the additional features of Damgaard is the same as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model” (NPL attached), which teaches: Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount of data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operating condition of the turbine. In this paper, the idea of deep learning is introduced into wind turbine condition monitoring. After selecting the variables by the method of the adaptive elastic network, the convolutional neural network (CNN) and the long and short term memory network (LSTM) are combined to establish the logical relationship between observed variables. Based on training data and hardware facilities, the method is used to process the temperature data of gearbox bearing. The purpose of artificial intelligence monitoring and over-temperature fault warning of the high-speed side of bearing is realized efficiently and conveniently. The example analysis experiments verify the high practicability and generalization of the proposed method.
US 20180357542, which teaches: A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features, The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.
US 20200201950, which teaches: An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629