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 .
Claim(s) 1-13 are pending for examination. Claim(s) 1 and 11-13 have been amended. This action is Final.
Response to Arguments
The claim interpretation under 35 U.S.C. 112(f) is withdrawn as the claim(s) have been amended.
Applicant's arguments filed 5/12/2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant Argues: Applicant respectfully submits that the amended claims do not recite a judicial exception and, in any event, integrate any alleged exception into a practical application and amount to significantly more than any alleged exception.
Examiner’s Response: The examiner respectfully disagrees for the reasons set forth below.
Applicant Argues: Under Prong One of Step 2A, the Office asserts that the claims recite "commercial interactions" or "legal interactions" "in the form of business relations." The "Certain Methods of Organizing Human Activity" sub-grouping the Office invokes is described in MPEP 2106.04(a)(2)(II) as encompassing "agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; [and] business relations" between commercial actors. The amended claims describe nothing of the sort. They recite a physical industrial system in which (i) at least one sensor monitors a component of a wind turbine generator and acquires detection data of the component, (ii) a controller including a processor executes a trained machine learning estimation model on detection data and a maintenance schedule to generate maintenance information, and (iii) the generated maintenance information is transmitted to a first terminal device. There is no agreement, no contract, no legal obligation, no advertising or marketing, no sales activity, and no relationship between commercial actors. The mere fact that a wind turbine generator may be owned or maintained in a commercial setting does not transform a sensor-based monitoring and machine-learning-based maintenance generation system into a "business relation" within the meaning of MPEP 2106.04(a)(2)(II). For the same reasons, the amended claims do not fall within either of the other two sub-groupings of MPEP 2106.04(a)(2)(II): no fundamental economic principle or practice is recited, and no managing of personal behavior or relationships or interactions between people is recited. The Office's characterization of the amended claims as a "business relation" is conclusory and untethered to the actual recited limitations.
Examiner’s Response: The examiner respectfully disagrees. The claims as recited the following limitations that are considered to be a business relation “monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator; an estimation model that ... includes the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information related to the maintenance for the wind turbine generated based on the detection data and the maintenance schedule using the estimation model; and transmit the generated maintenance information,” thus, would fall under Certain Methods of Organizing Human Activity. The examiner notes the generation of maintenance information based a model that used detected data and schedule as reasonably construed is a form business relation. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The Office's alternative characterization of the claims as reciting a "Mental Process" likewise does not survive the amendment. As an initial and dispositive matter, the amended claims recite "a detection unit including at least one sensor for monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator." A human cannot, as a practical matter, acquire vibration frequency data or generated-power data from a physical component of a wind turbine generator through a sensor. Spec. [0021]. Sensor-based acquisition of physical measurements from industrial equipment is not an observation, evaluation, judgment, or opinion of the kind that falls within MPEP 2106.04(a)(2)(III), and it cannot be performed in the human mind or with pen and paper. See MPEP 2106.04(a)(2)(III)(A); SRI Int'l, Inc. v. Cisco Sys., Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (claim does not recite a mental process where "the human mind is not equipped to" perform the recited limitations, such as detecting suspicious activity by using network monitors and analyzing network packets).
The remaining recited limitations independently confirm that the amended claims do not recite a mental process. The amended claims further recite "a controller including a processor that executes an estimation model that performs machine learning based on learning data including the detection data and a maintenance schedule as input data and maintenance information as ground truth data," and that the controller "generates the maintenance information ... using the trained estimation model." A human cannot, as a practical matter, train a neural-network-based estimation model on learning data structured as detection data and a maintenance schedule (input) against maintenance information (ground truth) by iteratively comparing the model's outputs to the ground truth and updating neural-network parameters until the degree of coincidence is within a prescribed range. Spec. [0032], [0034]-[0036]. Nor can a human practically execute the trained estimation model to generate maintenance information for a physical wind turbine generator. Spec. [0035], [0037]. The Office's mental-process analysis was directed to the originally recited steps of acquiring detection data, generating maintenance information based on the detection data, and transmitting the maintenance information, with the additional elements waved off as "unit, controller, device and further I/O interfaces and processor." The amended claims no longer recite generic acquisition, generation, and transmission steps, and the additional elements are no longer the generic computer components the Office characterized. The amended claims accordingly do not recite a mental process.
Examiner’s Response: The examiner respectfully disagrees. For example, the broadest reasonable interpretation of these limitations includes monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator; an estimation model that ... includes the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information related to the maintenance for the wind turbine generated based on the detection data and the maintenance schedule using the estimation model; and transmit the generated maintenance information, which, encompass steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. The examiner respectfully notes that a unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces are noted to be a recitation of generic computer components, thus, nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Even assuming, arguendo, that the amended claims recite a judicial exception, which Applicant does not concede, the additional elements integrate any such exception into a practical application under Step 2A, Prong Two. The Office's Prong Two analysis is directed only at the generic "unit, controller, device and further I/O interfaces and processor" recited in the original claim and concludes that those elements are described at "a high level" without "meaningful detail" and amount to "generic computer components." That analysis does not reach the additional elements recited in the amended claim, namely, a physical sensor monitoring a component of a wind turbine generator, a processor that executes a machine-learning estimation model trained on a specifically structured learning data set, and use of the trained estimation model to generate maintenance information.
Beyond that gap, the Office's "generic computer component" and "high level of generality" framing is precisely the type of analysis that the USPTO has expressly instructed examiners not to apply to machine-learning claims. The USPTO Memorandum, Advance Notice of Change to the MPEP in Light of Ex Parte Desjardins (December 5, 2025), revised MPEP § 2106.05(a) to instruct that:
When evaluating a claim as a whole, examiners should not dismiss additional elements as mere "generic computer components" without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system. See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (Appeals Review Panel Decision).
The same revision instructs that "Examiners and panels should not evaluate claims at such a high level of generality" that potentially meaningful technical limitations are dismissed without adequate explanation. Id. These instructions are directly applicable here, and the Office's existing Prong Two analysis does not comply with them.
The specification supplies the technical problem and the technical solution that the Desjardins memorandum directs examiners to credit. The specification identifies the technical problem of conventional wind turbine abnormality detection techniques failing to provide actionable maintenance information for eliminating an abnormality in the wind turbine generator. Spec. [0004]. The amended claims solve this problem through a specific ordered combination of elements: a sensor of a detection unit acquires detection data from a physical component of a wind turbine generator (e.g., vibration frequency of a bearing or the amount of electric power generated by a power generator, Spec. [0021]); a processor of a controller trains an estimation model on learning data structured as detection data and a maintenance schedule (input) against maintenance information (ground truth), iteratively adjusting parameters of a neural network to bring the degree of coincidence between estimated and ground-truth maintenance information within a prescribed range (Spec. [0032]-[0036]); the trained estimation model thereafter generates maintenance information for the wind turbine generator from new detection data and the maintenance schedule (Spec. [0035], [0037]); and the generated maintenance information is transmitted to a first terminal device for use by a maintenance recipient. Taken as an ordered combination, these limitations reflect concrete improvements to wind turbine generator maintenance technology disclosed in the specification.
The recited training of the estimation model by iteratively updating neural-network parameters based on the degree of coincidence between estimated and ground-truth maintenance information is directly analogous to the example added by the Desjardins memorandum at MPEP § 2106.05(a), subsection I, namely, "Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams." That example is now an enumerated illustration of features that may show an improvement in computer functionality. The amended claims are also analogous to claims that MPEP § 2106.04(d)(1), as revised by the Desjardins memorandum, identifies as examples of claims directed to a technological improvement and not directed to an abstract idea. See, e.g., SRI Int'l, Inc. v. Cisco Sys., Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in network technology and not directed to an abstract idea); Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (Appeals Review Panel Decision) (precedential) (claims to training a machine learning model were directed to improvements in machine learning technology). Just as in SRI, the recited sensor-based monitoring of a physical industrial component coupled with machine-learning-based generation of an actionable output cannot be reduced to generic computer activity. And as in Desjardins, the amended claims, evaluated as a whole, reflect specific improvements to the underlying technology, including the specific learning-data structure (detection data and maintenance schedule as inputs; maintenance information as ground truth) and specific use of the resulting trained model. The amended claims are accordingly directed to a practical application and are not "directed to" any alleged judicial exception under Step 2A.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes that purported improvement lies within he abstract idea itself, includes monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator; an estimation model that ... includes the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information related to the maintenance for the wind turbine generated based on the detection data and the maintenance schedule using the estimation model; and transmit the generated maintenance information and such limitations fall under Certain Methods of Organizing Human Activity and/or Mental Processes. The claims recite i.e., unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces and processor. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Applicant Argues: The same considerations carry the analysis through Step 2B, and the Office's Step 2B finding is independently deficient. The recited combination of (i) sensor-based detection of physical wind turbine generator component data, (ii) machine-learning training of an estimation model on a specifically structured learning data set, (iii) generation of maintenance information using the trained estimation model, and (iv) transmission of the generated maintenance information to a first terminal device amounts to significantly more than any alleged judicial exception. See MPEP 2106.05. The Office has not addressed, much less established with factual support, that this particular combination is well-understood, routine, and conventional in the technical field of wind turbine generator maintenance. Such factual support is required. See Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018); MPEP 2106.05(d); USPTO Memorandum on Berkheimer v. HP Inc. (Apr. 19, 2018). The Office's Step 2B paragraph rests entirely on the conclusory observation that "unit, controller, device and further 11O interfaces and processor" are generic computer components, which is the same analysis that the Desjardins memorandum's revision of MPEP § 2106.05(a) instructs examiners not to apply without considering whether the additional elements confer a technological improvement. For this independent reason as well, the Step 2B rejection should be withdrawn.
For at least the similar reasons discussed above with respect to independent claim 1, Applicant respectfully submits that independent claims 11, 12, and 13 and the remaining claims depending from independent claim 1 are patent eligible. Reconsideration and withdrawal of the rejection under 35 U.S.C. § 101 are respectfully requested.
Examiner’s Response: The examiner respectfully disagrees. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. The additional elements, i.e., unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces and processor, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The examiner notes these elements were never noted to be well-understood, routine, and conventional, thus, Berkheimer evidentiary requirement is noted needed. Therefore, under Step 2B, there are no meaningful limitations that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Applicant’s arguments filed 5/12/2026 with respect to the 35 U.S.C. 102 rejection have been considered but are moot in view of new grounds of rejection.
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.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-13 are directed to a machine and/or process. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: Regarding claim 1, and similar claim(s) 11-13, taken as representative, recites at least the following limitations that recite an abstract idea:
A management system comprising:
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 11-13 includes monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator; an estimation model that ... includes the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information related to the maintenance for the wind turbine generated based on the detection data and the maintenance schedule using the estimation model; and transmit the generated maintenance information, thus, claim 1, and similar claim(s) 11-13 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" or "legal interactions" in the form of business relations.
The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III), in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and similar claim(s) 11-13, i.e., unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 11-13, includes monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator; an estimation model that ... includes the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information related to the maintenance for the wind turbine generated based on the detection data and the maintenance schedule using the estimation model; and transmit the generated maintenance information, which, encompass steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and similar claim(s) 11-13, recite i.e., unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces and processor. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 11-13 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 11-13 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 11-13 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 11-13, i.e., unit including at least one sensor, controller including a processor, performing/learning/trained machine learning, device and further I/O interfaces and processor; thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 11-13 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 11-13 is ineligible.
Regarding Claims 2-10, claims 2-10 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations i.e., further features related to maintenance “management” and/or further recite “Mental Processes” as the claims recite further concepts that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (i.e., claim 10 – display device displaying captured image by an imaging device). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-7, 9, and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Son et al. (US 2017/0352010 A1) in view of Dobashi et al. (US 2021/0003399 A1).
Regarding Claim 1;
Son discloses a management system ([0011] - ...a wind farm supervision monitoring system... and [0013]) comprising:
a detection unit including at least one... for monitoring a component of a wind turbine generator to acquire detection data of the component of the wind turbine generator ([0054]-[0056] - The data collection unit 210 collects data about status monitoring of each wind turbine from at least one site server 260 to 264. Here, the data collection unit 210 may set special conditions, for example, conditions on how fast a wind speed is, which direction a wind direction is, whether an outside situation corresponds to a specific condition, or the like and may collect data only when the wind turbine corresponds to the setting. The abnormality status detection unit 220 detects the abnormality status of each wind turbine based on the collected data about the status monitoring of each wind turbine and issues an alarm. The wind data management unit 230 may early detect a fault of each wind turbine or monitor performance of each wind turbine, based on the data about the status monitoring of each wind turbine provided from at least one site server 260 to 264 or the data about the abnormality status of each wind turbine detected by the abnormality status detection unit 220 and [0105]-[0106] and [0121] - part);
a controller including a processor that... generates [the] maintenance information related to maintenance for the wind turbine generator based on the detection data ([0013] - ... a supervision unit configured to manage a turbine operation status and operation and maintenance of each wind turbine and provide information for establishing an operation and maintenance plan for the detected abnormality status of the wind turbine and [0121]); and
a first terminal device, wherein the controller is configured to transmit the generated maintenance information to the first terminal device ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121]).
Son fails to explicitly disclose
...at least one sensor for monitoring a component...;
a controller including a processor that executes an estimation model that performs machine learning based on learning data including the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information... based on the detection data and the maintenance schedule using the trained estimation model.
However, in an analogous art, Dobashi teaches:
...at least one sensor for monitoring a component... ([0039] - A sensor unit 207 includes one or more sensors, such as sensors for measuring the operation of power generation units (not shown) in the mobile body);
a controller including a processor that executes an estimation model that performs machine learning based on learning data including the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information... based on the detection data and the maintenance schedule using the trained estimation model ([0076] - “The optimal maintenance time based on the deterioration state of the mobile body” is information set by the maintenance worker. For example, this is date information indicating a maintenance time as late as possible and capable of preventing a failure with respect to the state of the mobile body. This information provides a ground truth label for the information generating server 107 to estimate the optimal maintenance time in response to an input of the usage state information and [0083] - In S303, the control unit 301 estimates the maintenance time for each mobile body. Specifically, first, the maintenance time estimating unit 310 trains a neural network that takes, as an input, usage state information about a mobile body and outputs a maintenance time. For example, a neural network (simply referred to as an N-network) having multiple hidden layers is used here. At the start of training, the N-network initializes the weight on the N-network to a random value and [0084]-[0085] - For example, a piece of input data obtained from one mobile body (e.g., the usage state information for the period up to maintenance1) is given as an input to the N-network, which then outputs an estimated maintenance time as a result. The weight on the N-network is adjusted so that this estimated result approaches the correct maintenance time obtained from the maintenance information (e.g., “the optimal maintenance time based on the deterioration state of the mobile body” recorded in maintenance1)... The maintenance time estimating unit 310 then inputs the most recent usage state information about a mobile body (for the period from the last maintenance to the present) to the trained N-network to estimate a future maintenance time.).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Dobashi to the system/ wind turbine generator of Son to include concepts of ...at least one sensor for monitoring a component...; a controller including a processor that executes an estimation model that performs machine learning based on learning data including the detection data and a maintenance schedule as input data and maintenance information as ground truth data, and generates the maintenance information... based on the detection data and the maintenance schedule using the trained estimation model.
One would have been motivated to combine the teachings of Dobashi to Son to do so as it provides / allows information for a worker responsible for maintenance [to] work to more efficiently (Dobashi, [0010]).
Regarding Claim 2;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the controller is configured to specify a maintenance type of the wind turbine generator based on the detection data ([0064] - ... the supervision unit 240 may determine one of a run to failure that performs maintenance after the operation until a major part breaks, periodic maintenance that periodically performs maintenance for a predetermined period, and status based maintenance that acquires a facility status based on a facility diagnosis technology or a status monitoring technology to early detect a fault and tracks the progress to predict the next progress to thereby perform the operation and maintenance at a specific time, thereby establishing the operation and maintenance plan), and the maintenance information includes information indicating the maintenance type ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work).
Regarding Claim 3;
Son in view of Dobashi discloses the management system according to claim 2.
Son further discloses, wherein the maintenance type includes at least one of a repair of a component of the wind turbine generator, a position adjustment of the component, and an inspection of the component ([0064] - ... the supervision unit 240 may determine one of a run to failure that performs maintenance after the operation until a major part breaks...)
Regarding Claim 4;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the controller is configured to specify a maintenance part of the wind turbine generator based on the detection data ([0064] - ... the supervision unit 240 may determine one of a run to failure that performs maintenance after the operation until a major part breaks...), and the maintenance information includes information indicating the maintenance part ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work).
Regarding Claim 5;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the controller is configured to specify a tool and a component necessary for maintenance for the wind turbine generator based on the detection data ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work), and the maintenance information includes information indicating the tool and the component ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work).
Regarding Claim 6;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the controller is configured to specify a maintenance timing for the wind turbine generator based on the detection data ([0064] - ... the supervision unit 240 may determine one of a run to failure that performs maintenance after the operation until a major part breaks, periodic maintenance that periodically performs maintenance for a predetermined period, and status based maintenance that acquires a facility status based on a facility diagnosis technology or a status monitoring technology to early detect a fault and tracks the progress to predict the next progress to thereby perform the operation and maintenance at a specific time, thereby establishing the operation and maintenance plan),, and the maintenance information includes information indicating the maintenance timing ([0016] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work)..
Regarding Claim 7;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the controller is configured to specify an abnormal part in the wind turbine generator based on the detection data ([0055]-[0057] - The wind data management unit 230 may early detect a fault of each wind turbine or monitor performance of each wind turbine, based on the data about the status monitoring of each wind turbine provided from at least one site server 260 to 264 or the data about the abnormality status of each wind turbine detected by the abnormality status detection unit 220. The supervision unit 240 may manage a turbine operation status and operation and maintenance of each wind turbine and provide information for establishing an operation and maintenance plan for the detected abnormality status of the wind turbine and [0064] - ...status based maintenance that acquires a facility status based on a facility diagnosis technology or a status monitoring technology to early detect a fault and tracks the progress to predict the next progress to thereby perform the operation and maintenance at a specific time and [0144] - or the like when the operation and maintenance (O & M) for managing at least one wind farm is performed, acquiring the data from the plurality of wind farms based on the management control data acquisition (SCADA) and the status monitoring system (CMS) to early detect the failure of parts and prevent the critical accident, sharing the turbine operation status), and the maintenance information includes information indicating the abnormal part ([0014] - The supervision unit may collect an early alarm, a weather forecast, and component and tool status information using the SCADA data and the CMS data provided from the at least one site server to establish an operation and maintenance plan, automatically issue a work ticket suggesting a work that the worker needs to perform based on the established maintenance plan, receive a registration of a work record depending on the work ticket, and perform a cooperative work for the customer request and answer of a helpdesk and [0064] and [0117]-[0119] - Further, for the mobile application menu, the supervision unit 240 may provide the supervision, operation and maintenance (O & M), and helpdesk function so that the mobile terminal may confirm the operation status of the wind turbine and [0121] - Further, for the plan maintenance, the run to failure, and the prediction maintenance, the work history for each part may include the work plan function of inquiring and registering the work plan, the work record function of inquiring the work record input to the issued work ticket, and the equipment tool function of inquiring the equipment and tool used for work).
Regarding Claim 9;
Son in view of Dobashi discloses the management system according to claim 1.
Son further discloses wherein the first terminal device is a portable terminal device ([0037] - FIGS. 13(a) to 13(d) are diagrams illustrating an example of providing a turbine operation status within a wind farm to a mobile device, according to an embodiment of the present disclosure.)
Regarding Claim(s) 11; claim(s) 11 is/are directed to a/an controller associated with the system claimed in claim(s) 1. Claim(s) 11 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 12; claim(s) 12 is/are directed to a/an device associated with the system claimed in claim(s) 1. Claim(s) 12 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 13; claim(s) 13 is/are directed to a/an method associated with the system claimed in claim(s) 1. Claim(s) 13 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Son et al. (US 2017/0352010 A1) in view of Dobashi et al. (US 2021/0003399 A1) and further in view of Ikeda et al. (US 2015/0116131 A1).
Regarding Claim 8;
Son in view of Dobashi discloses the management system according to claim 1.
Son in view of Dobashi fails to explicitly disclose further comprising a second terminal device, wherein the controller is configured to specify a degree of abnormality in the wind turbine generator based on the detection data, and the controller is configured to transmit the maintenance information to one of the first terminal device and the second terminal device according to the degree of abnormality.
However, in an analogous art, Ikdea further teaches further comprising a second terminal device (FIG. 1 – Monitoring Terminals (multiple depicted)) and wherein the controller is configured to specify a degree of abnormality in the wind turbine generator based on the detection data ([0073] - Monitoring terminal 340 transmits the designated diagnostic operation conditions to data server 330 (step S13), which then causes the diagnostic operation conditions to be stored in data server 330 (step S14) and [0075] - The learning period refers to a period for generating a threshold value for determining the condition of each apparatus of wind turbine 10, after the passage of the above-described basic data collection period, which is needed to determine the diagnostic operation conditions of wind turbine 10 and [0083] - . Where the measurement data exceeds threshold value WN, monitoring terminal 340 displays a sign such as "WARNING", for example, indicating that the corresponding apparatus is in a condition with a higher degree of abnormality), and the controller is configured to transmit the maintenance information to one of the first terminal device and the second terminal device according to the degree of abnormality (FIG. 1 and [0083]).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Ikeda to the system of Son in view of Dobashi to include further comprising a second terminal device, wherein the controller is configured to specify a degree of abnormality in the wind turbine generator based on the detection data, and the controller is configured to transmit the maintenance information to one of the first terminal device and the second terminal device according to the degree of abnormality.
One would have been motivated to combine the teachings of Ikeda to Son in view of Dobashi to do so as it provides / allows a condition monitoring system capable of correctly diagnosing an abnormality of an apparatus included in a wind turbine (Ikeda, [0008]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Son et al. (US 2017/0352010 A1) in view of Dobashi et al. (US 2021/0003399 A1) and further in view of Thronicke (US 2021/0374945 A1).
Regarding Claim 10;
Son in view of Dobashi discloses the management system according to claim 9.
Son in view of Dobashi fails to explicitly disclose wherein the first terminal device includes an imaging device that captures an image of the wind turbine generator, a display device that shows a captured image captured by the imaging device, and a display controller, wherein the display controller displays the captured image of a maintenance part in the wind turbine generator in a manner different from a manner of display of the captured image of each of other parts in the wind turbine generator.
However, in an analogous art, Thronicke wherein the first terminal device includes an imaging device that captures an image of the wind turbine generator ([0027] - The system 1 can e.g. be part of a mobile apparatus. The system can e.g. be part of a vehicle or a drone which can be used for the purpose of the detection of errors associated with the object 2. However, the system 1 may be also part of an immobile apparatus, e.g. a stationary surveillance system and [0031] and [0036] - Optionally, the system 1 may also comprise a communication module 7. The communication module 1 can be designed to transmit information about detected errors and/or the captured input data to a controlling station for further examination and [0037]), a display device that shows a captured image captured by the imaging device, and a display controller, wherein the display controller displays the captured image of a maintenance part in the wind turbine generator in a manner different from a manner of display of the captured image of each of other parts in the wind turbine generator ([0031] and [0036] - Optionally, the system 1 may also comprise a communication module 7. The communication module 1 can be designed to transmit information about detected errors and/or the captured input data to a controlling station for further examination and [0039] - In a first method step 11 of the method 9 the at least one parameter is monitored. The specified range of the parameter defines the context within which a result of a recognition of at least parts of the object is expected.)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Thronicke to the system of Son in view of Dobashi to include wherein the first terminal device includes an imaging device that captures an image of the wind turbine generator, a display device that shows a captured image captured by the imaging device, and a display controller, wherein the display controller displays the captured image of a maintenance part in the wind turbine generator in a manner different from a manner of display of the captured image of each of other parts in the wind turbine generator.
One would have been motivated to combine the teachings of Thronicke to Son in view of Dobashi to do so as it provides / allows a, improved method for examining an object for errors (Thronicke, [0004]).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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/ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626
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