DETAILED ACTION
The following is a FINAL office action upon examination of the application number 18/540452.
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 .
Response to Amendment
Claims 1, 12, and 20 have been amended.
Claims 1-20 are pending in the application and have been examined on the merits discussed below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 12, and 20 have been amended to recite “a machine learning model trained using a multi-variate anomaly detection algorithm.” Paragraphs 18, describe a machine learning model trained on historical sensor data ([0018] …the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents). Paragraph 58 describes the application of a multi-variate anomaly detection algorithm to sensors ([0058] In some embodiments, the renewable energy asset monitoring system 104 may apply a multi-variate anomaly detection algorithm to sensors that are monitoring operating conditions of any number of renewable assets (e.g., wind turbines and or solar generators). The renewable energy asset monitoring system 104 may remove data associated with a past, actual failure of the system (e.g., of any number of components and or devices) or increased risk of failure, therefore highlighting subtle anomalies from normal operational conditions that lead to actual failures). However, Examiner cannot find any description of a machine learning model trained using a multi-variate anomaly detection algorithm. Therefore, Examiner finds that the claims recite limitations that were not described in the specification in such a way to reasonably convey that the inventor had possession of the claimed invention. Appropriate correction/clarification is required. Dependent claims inherit the deficiencies from their respective parent claims.
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 1-20 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.
(Step 1) Claims 1-11 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention. Claims 12-19 are directed to a system comprising at least one processor; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claim 20 is directed to a method; thus this claim is directed to a process, which is one of the statutory categories of invention.
(Step 2A) The claims recite an abstract idea instructing how to generate and display health information for wind turbines, which is described by claim limitations reciting:
receiving first current sensor data of a first time period …, the first current sensor data including sensor data from … the gearbox subcomponents and the generator subcomponents;
determining health indicators for the gearbox subcomponents by applying a … model trained using a multi-variate anomaly detection algorithm that removes data associated with past failures to highlight subtle anomalies from normal operation conditions, the health indicators corresponding to alerts for current or predicted problems of the gearbox subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a … model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents, wherein the multi-variate anomaly detection algorithm utilizes curated data feature including temperature rise in the gearbox with regards to power generation, reactive power, and ambient temperature to improve accuracy of detection;
determining health indicators for the generator subcomponents by applying the … model trained using the multi-variate anomaly detection algorithm, the health indicators corresponding to alerts for current or predicted problems of the generator subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a .. model trained on second historical sensor data of a second time period, including sensor data from … the generator subcomponents;
receiving the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents; and
displaying a list of the multiple wind turbines, the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents, the list being sortable by health indicators for the gearbox subcomponents and/or by the health indicators for the generator subcomponents, and the list being filterable by alerts for the gearbox subcomponents and/or alerts for the generator subcomponents.
The identified limitations in the claims describing generating and displaying health information for wind turbines (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices. Additionally limitations reciting a multi-variate anomaly detection algorithm also fall within the “Mathematical Concepts” grouping of abstract ideas. Dependent claims 2, 3, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, and 19, recite limitations that further narrow the abstract idea (i.e., generating and displaying health information for wind turbines); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the non-transitory computer readable medium comprising executable instructions, the executable instructions being executable by one or more processors in claim 1, and the at least one processor; and memory containing instructions, the instructions being executable by the at least one processor in claim 12, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as receiving first current sensor data … from multiple wind turbines in one or more wind turbine farms in one or more geographies and sensor data from sensors monitoring do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additional elements reciting a wind turbine including a gearbox and a generator, the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent do not provide an improvement and only add machinery in its ordinary capacity to apply the abstract idea.
Additional elements reciting determining … by applying the machine learning model trained … and … alerts being generated by a machine learning model… do not improve the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment (machine learning). Additional elements in claims 5 and 16, related to sending the work order to a work order system do not provide an improvement and only add insignificant extra-solution activities. Additional elements in claim 8 related to a zoomable and scrollable map only generally link the abstract idea to a technological environment and fail to provide an improvement to the computer or technology. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see Spec. [0053][0207][0223]. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as receiving first current sensor data … from multiple wind turbines in one or more wind turbine farms in one or more geographies and sensor data from sensors monitoring do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additional elements reciting a wind turbine including a gearbox and a generator, the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent do not provide an improvement and only add machinery in its ordinary capacity to apply the abstract idea. Additional elements reciting determining … by applying the machine learning model trained … and … alerts being generated by a machine learning model… do not improve the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Additional elements in claims 5 and 16, related to sending the work order to a work order system do not provide an improvement and only add insignificant extra-solution activities (data transmission). Additional elements in claim 8 related to a zoomable and scrollable map only generally link the abstract idea to a technological environment and fail to provide an improvement to the computer or technology. Additionally, with respect to data gathering/transmission limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
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.
Claim(s) 1-4, 7, 9, 10, 12-15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0203157 (Visweswariah); in view of US 2021/0182749 (DT360).
As per claim 1 , Visweswariah teaches: a non-transitory computer readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising: ([0004] An example nontransitory computer readable medium comprises executable instructions. The executable instructions are executable by one or more processors to perform a method)
receiving first current sensor data of a first time period from multiple wind turbines in one or more wind turbine farms in one or more geographies, ([0010] An example method comprises receiving historical wind turbine component failure data and wind turbine asset data from the one or more SCADA systems [0078] … As shown in FIG. 3, there may be any number of wind turbines in a wind farm)
a wind turbine including a gearbox and a generator, ([0068] FIG. 2 depicts components that often produce failures of wind turbines and components that often produce failures in solar panel generators. Failures in wind turbines often occur as a result of failures in a main bearing 202 (e.g., a main bearing shaft), gearbox 204, generator 206, or anemometer 208)
the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent; ([0068] Failures in wind turbines often occur as a result of failures in a main bearing 202 (e.g., a main bearing shaft), gearbox 204, generator 206 [0071] bearings of a gearbox [0072] Failures often occur in bearings, stator, rotor, or the like can lead to inconsistent voltage to total failure [0086] the component failure prediction system 104 may select a failure prediction model of a set of failure prediction models for each component type (e.g., bearing), component (e.g., specific bearing(s) in one or more assets), component group type (e.g., generator including two or more components), component group (e.g., specific generator(s) including two or more components in one or more assets))
the first current sensor data including sensor data from sensors monitoring the gearbox subcomponents and the generator subcomponents; ([0004] the first sensor data including sensor data from the one or more sensors of the one or more components [the gearbox subcomponents and the generator subcomponents] of the renewable energy asset [0199] In step 2012, the model application module 516 may receive current sensor data from the same components or group of components [0254] The communication module 502 may be configured to receive failure data, health data, asset data (e.g., WT failure data & asset data), sensor data, and SCADA information (See phase 1 of FIG. 6))
determining health indicators for the gearbox subcomponents by applying a machine learning model trained using a multi-variate anomaly detection algorithm that removes data associated with past failures to highlight subtle anomalies from normal operation conditions, the health indicators corresponding to alerts for current or predicted problems of the gearbox subcomponents with varying lead time, …, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents ([0012] applying the at least one machine learning model to the sensor data to generate a first failure prediction a failure of at least one component of the one or more components ... generate a list of renewable energy assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, the alert indicating the at least one component of the one or more components and information regarding the failure prediction [0063] the component failure prediction system 104 may be used to generate and evaluate multiple models using the same historical sensor data but each with different lengths of time prior to predicted failure in order to identify at least one model with an acceptable accuracy at an acceptable prediction time before component failure is expected to occur [0044] FIG. 20 is a flowchart for predicting failures and/or potential failures of renewable energy assets [0177] FIG. 17 a depicts three classes across three periods of time for gearbox failure in one example; varying lead time. [0185] different alerts may be generated based on different possible failure and or different failure states. For example, some failure states may be more serious than others, as such more alerts and/or additional detailed alerts may be provided to a larger number of digital devices ... depending on the seriousness, significance, and/or imminence of failure [0088] … may apply a multi-variate anomaly detection algorithm to sensors that are monitoring operating conditions of any number of renewable assets (e.g., wind turbines and or solar generators). The component failure prediction system 104 may remove data associated with a past, actual failure of the system (e.g. of any number of components and or devices), therefore highlighting subtle anomalies from normal operational conditions that lead to actual failures.)
wherein the multi-variate anomaly detection algorithm utilizes curated data feature including temperature rise in the gearbox with regards to power generation, reactive power, and ambient temperature to improve accuracy of detection; ([0088] … may apply a multi-variate anomaly detection algorithm to sensors that are monitoring operating conditions of any number of renewable assets (e.g., wind turbines and or solar generators). The component failure prediction system 104 may remove data associated with a past, actual failure of the system (e.g. of any number of components and or devices), therefore highlighting subtle anomalies from normal operational conditions that lead to actual failures. [0248] … failure detection, for example, may utilize temperature rise in the gearbox with regards to power generation, reactive power, and ambient temperature).
determining health indicators for the generator subcomponents by applying the machine learning model trained using the multi-variate anomaly detection algorithm, the health indicators corresponding to alerts for current or predicted problems of the generator subcomponents with varying lead time, …, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from sensors monitoring the generator subcomponents; ([0012] applying the at least one machine learning model to the sensor data to generate a first failure prediction a failure of at least one component of the one or more components ... generate a list of renewable energy assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, the alert indicating the at least one component of the one or more components and information regarding the failure prediction; [0063] the component failure prediction system 104 may be used to generate and evaluate multiple models using the same hist01ical sensor data but each with different lengths of time prior to predicted failure in order to identify at least one model with an acceptable accuracy at an acceptable prediction time before component failure is expected to occur [0177] FIG. 17b depicts three classes across three periods of time for generator failure in one example; [0185] different alerts may be generated based on different possible failure and or different failure states. For example, some failure states may be more serious than others, as such more alerts and/or additional detailed alerts may be provided to a larger number of digital devices ... depending on the seriousness, significance, and/or imminence of failure)
receiving the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents; and ([0012] generate a list of renewable energy assets that are operating within a healthy threshold [0201] In step 2016, the report and alert generation module 520 may generate an alert if a trigger condition is satisfied ... The alert may be issued by text, SMS, email, instant message, phone call, and/or the like. The ale1t may indicate the component, component group, type of component, type of component group, and/or the like that triggered the prediction as well as any information relevant to the prediction, like percentage of confidence and predicted time frame; [0254] The communication module 502 may be configured to receive failure data, health data, asset data (e.g., WT failure data & asset data), sensor data, and SCADA information [0256] the health data includes an asset identifier (e.g., turbine identifier), and sensor data of components or a group of components of the asset identified by the asset identifier [0338] healthy assets and/or components (e.g., those with health scores above or at a particular health threshold) may be removed from consideration or filtered to assist in assessing those assets and/or components that require reactive services to prevent potential failure)
displaying a list of the multiple wind turbines, the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents, the list being sortable by health indicators for the gearbox subcomponents and/or by the health indicators for the generator subcomponents, and the list being filterable by alerts for the gearbox subcomponents and/or alerts for the generator subcomponents ([Fig 26][Fig 28][0011] determining healthy assets of the any number of renewable energy assets by comparing one or more signals from the one or more SCAD A systems to known healthy operating signals [0012] generate a list of renewable energy assets that are operating within a healthy threshold ... updating a list of renewable energy assets to perform surveillance based on the list of renewable energy assets that are operating within a healthy threshold [0257] health data may be received from wind turbines. For example, health data may include but is not be limited to a turbine identifier (e.g., Turbineid) that identifies a wind turbine or group of wind turbines, wind turbine asset data, and sensor data [0332] In phase 7 of FIG. 26, the health condition score module 2504 may generate a health condition score by converting the health assessment into a monotonic health condition score [0333] If the health condition score is above a health condition threshold, the asset or components may be removed from a schedule of routine maintenance. In some embodiments, if the health condition score is above a health condition threshold, the asset or components may be removed from a schedule for surveillance or reduced in priority relative to other assets and/or components that are not determined to be in a health condition).
Although not explicitly taught by Visweswariah, DT360 teaches: the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, ([0060] Based on the above analysis, the final severity index of the asset is determined as ranging from 0 to 1 and is marked using a color scale from red for a high value (around 1), to amber indicating an intermediate value (around 0.75), and then to green indicating a low value (around 0)).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Visweswariah with the aforementioned teachings of DT360 with the motivation of determining the severity index for assets (DT360 [0060]). Further, one of ordinary skill in the art would have recognized that applying the teachings of DT360 to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of different severity levels.
As per claim 2, Visweswariah teaches: receiving a selection to filter the list of the multiple wind turbines by one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent ([0266] the data preparation module 506 may perform ... event log-based filtering [0015] …filtering the one or more renewable energy assets that are operating within the health threshold)
filtering the list of the multiple wind turbines to include wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent ([0338] during the hybrid condition-based assessment, healthy assets and/or components (e.g., those with health scores above or at a particular health threshold) may be removed from consideration or filtered to assist in assessing those assets and/or components that require reactive services to prevent potential failure [0255] Health data may indicate healthy operation of a component or combination of components. As discussed herein, if SCADA data indicates that an asset is sufficiently healthy (e.g., not likely to fail or if the SCADA data is within expected parameters), then that particular asset may be removed from a maintenance schedule and/or removed from a list of assets that may require supervision or surveillance (or, alternately, the priority for surveillance may be reduced relative to other assets) [0341] A third stage may be to determine monitoring of assets or components. In this example, potentially failing assets and/or component(s) may be monitored more aggressively (e.g., watched and assessed more frequently or alert conditions may be triggered by a more stringent crite1ia) as compared to monitoring for those assets and/or component(s) that are potentially healthy may be reduced in priority or removed from being scheduled for surveillance)
displaying in the list wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent ([0201] The alert may be issued by text, SMS, email, instant message, phone call, and/or the like. The alert may indicate the component, component group, type of component, type of component group).
As per claim 3, Visweswariah teaches: receiving a selection of a wind turbine, the wind turbine having a health indicator for a gearbox subcomponent or a generator subcomponent corresponding to … a … severity risk alert… ([0257) health data may be received from wind turbines. For example, health data may include but is not be limited to a turbine identifier (e.g., Turbineid) that identifies a wind turbine [0256] the health data includes an asset identifier (e.g., turbine identifier), and sensor data of components or a group of components of the asset identified by the asset identifier [0107] The alarm metadata may include, but is not limited to, an event code (e.g., EventCode), description, and the like. [0108] FIG. 8a depicts an example event log. The event log includes a turbine identifier [0109] In this example, the same wind turbine is undergoing four different events, including a change in wind speed, a change in pitch, a remote power setpoint change, and a generator outage [0185] different alerts may be generated based on different possible failure and or different failure states. For example, some failure states may be more serious than others, as such more ale11s and/or additional detailed alerts may be provided to a larger number of digital devices ... depending on the seriousness, significance, and/or imminence of failure)
receiving alert status and date information for the gearbox subcomponent or the generator subcomponent of the wind turbine; and ([0201] The alert may indicate the component, component group, type of component, type of component group, and/or the like that triggered the prediction [0206] In this example, the same wind turbine is undergoing four different events, including a change in wind speed, a change in pitch, a remote power setpoint change, and a generator outage [0204] The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode), event type (e.g., EventType), event start time (e.g., EventStartTime), event end time (e.g., EventEndTime))
displaying the health indicator, the alert status and the date information for the gearbox subcomponent or the generator subcomponent of the wind turbine ([0201] The alert may be issued by text, SMS, email, instant message ... The alert may indicate the component, component group, type of component, type of component group [0110] FIG. 8b depicts example alarm metadata [0204] In step 2102, the data extraction module 504 may receive event and alarm data from one or more SCAD A systems used to supervise and monitor any number of wind turbines. The data extraction module 504 may include an input interface to receive detailed event and alarm logs as well as event and alarm metadata. The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode), event type (e.g., EventType), event start time (e.g., EventStartTime), event end time (e.g., EventEndTime), component, subcomponent, and/or the like. The turbine identifier may be an identifier that identifies a particular wind turbine or group of turbines. An event code may be a code that indicates an event associated with performance or health of the particular wind turbine or group of turbines. The event type may be a classification of performance or health. An event start time may be a particular time that an event (e.g., an occurrence that affects performance or health) began and an event end time may be a particular time that the event ended. Components and subcomponents may include identifiers that identify one or more components or subcomponents that may be affected by the event).
Although not explicitly taught by Visweswariah, DT360 teaches: a low severity risk alert, a medium severity risk alert, or a high severity risk alert ([0060] Based on the above analysis, the final severity index of the asset is determined as ranging from 0 to 1 and is marked using a color scale from red for a high value (around 1), to amber indicating an intermediate value (around 0.75), and then to green indicating a low value (around 0)).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Visweswariah with the aforementioned teachings of DT360 with the motivation of determining the severity index for assets (DT360 [0060]). Further, one of ordinary skill in the art would have recognized that applying the teachings of DT360 to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of different severity levels.
As per claim 4, Visweswariah teaches: receiving a selection of the gearbox of the wind turbine; ([0177) FIG. 17a depicts three classes across three periods of time for gearbox failure in one example)
determining an overall health indicator of the gearbox, the overall health indicator based at least in part upon any alerts for the gearbox subcomponents; ([0254] The communication module 502 may be configured to receive failure data, health data, asset data (e.g., WT failure data & asset data), sensor data, and SCADA information (See phase 1 of FIG. 6). Failure data may indicate failure of a component or combination of components [0204] An event code may be a code that indicates an event associated with performance or health of the particular wind turbine or group of turbines. The event type may be a classification of performance or health. An event start time may be a particular time that an event (e.g., an occurrence that affects performance or health) began and an event end time may be a particular time that the event ended. Components and subcomponents may include identifiers that identify one or more components or subcomponents that may be affected by the event [0256] the health data includes an asset identifier (e.g., turbine identifier), and sensor data of components or a group of components of the asset identified by the asset identifier [0106] The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode), event type (e.g., EventType), event start time (e.g., EventStartTime), event end time (e.g., EventEndTime), component, subcomponent; gearbox subcomponents);
displaying the overall health indicator of the gearbox; ([0201] In step 2016, the report and alert generation module 520 may generate an alert if a trigger condition is satisfied ... The alert may be issued by text, SMS, email, instant message. The alert may indicate the component, component group, type of component, type of component group, and/or the like that triggered the prediction as well as any information relevant to the prediction, like percentage of confidence and predicted time frame [0204] An event code may be a code that indicates an event associated with performance or health of the particular wind turbine or group of turbines. The event type may be a classification of performance or health. An event start time may be a particular time that an event (e.g., an occurrence that affects performance or health) began and an event end time may be a particular time that the event ended. Components and subcomponents may include identifiers that identify one or more components or subcomponents that may be affected by the event)
determining an overall health indicator of the generator, the overall health indicator based at least in part upon any alerts for the generator subcomponents; and ([0204] In step 2102, the data extraction module 504 may receive event and alarm data from one or more SCADA systems used to supervise and monitor any number of wind turbines. The data extraction module 504 may include an input interface to receive detailed event and alarm logs as well as event and alarm metadata. The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode) [0256] the health data includes an asset identifier (e.g., turbine identifier), and sensor data of components or a group of components of the asset identified by the asset identifier [0106] The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode), event type (e.g., EventType), event start time (e.g., EventStartTime), event end time (e.g., EventEndTime), component, subcomponent)
displaying the overall health indicator of the generator ([0201] In step 2016, the report and alert generation module 520 may generate an alert if a trigger condition is satisfied ... The alert may be issued by text, SMS, email, instant message. The alert may indicate the component, component group, type of component, type of component group, and/or the like that triggered the prediction as well as any information relevant to the prediction, like percentage of confidence and predicted time frame [0204] An event code may be a code that indicates an event associated with performance or health of the particular wind turbine or group of turbines. The event type may be a classification of performance or health. An event start time may be a particular time that an event (e.g., an occurrence that affects performance or health) began and an event end time may be a particular time that the event ended. Components and subcomponents may include identifiers that identify one or more components or subcomponents that may be affected by the event)
As per claim 7, Visweswariah teaches: receiving a selection of the gearbox or the generator; ([0177] FIG. 17a depicts three classes across three periods of time for gearbox failure in one example [Fig. 11])
determining which gearbox subcomponents or generator subcomponents are monitored by sensors and which gearbox subcomponents or generator subcomponents are not monitored by sensors; ([0141] For example, the communication module 502 may receive 35 channels of sensor data (e.g., from thirty five sensors identified in FIG. 11)) and which gearbox subcomponents or generator subcomponents are not monitored by sensors ([0013] The method may further comprise detecting missing sensor data and replacing the missing sensor data with a linear interpolation [0119] There may be two types of missing values in SCADA sensor signals. The first type of missing values in SCADA sensor signals is when one sensor has missing values for the whole observation window or a portion within the observation window[0121] The second type of missing values in SCADA sensor signals is when all sensors are missing values (probably as a result of communication loss)
displaying a list of the gearbox subcomponents or generator subcomponents, for the gearbox subcomponents or generator subcomponents that are monitored by sensors, the health indicators for the gearbox subcomponents or generator subcomponents ([Fig. 11]), and for the gearbox subcomponents or generator subcomponents that are not monitored by sensors, indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors; and ([Fig 10a] [Fig. 10b] [0030] FIG. 10b is an example of missing sensor values.)
displaying a cross-sectional outline view of the outline of the gearbox or generator, the outlines of the gearbox subcomponents or generator subcomponents, the health indicators of the gearbox subcomponents or generator subcomponents that are monitored by sensors, ([Fig. 11][Fig 17c]) and the indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors ([Fig. 10a][Fig. 10b][Fig. 17c]).
As per claim 9, Visweswariah teaches: receiving a selection of a gearbox subcomponent alert or a generator subcomponent alert; ([Fig. 11] [0177] FIG. 17a depicts three classes across three periods of time for gearbox failure in one example)
receiving time series data for the selected gearbox subcomponent; ([0092] The component failure prediction system 104 may receive sensor data regarding any number of components from any number of devices, such as wind turbines from a wind farm. The sensor data may include multivariate timeseries data);
displaying the time series data in one or more data charts ([0105] The sensor data may be a multi-variant time series. FIG. 7 is an example of sensor data from a SCADA system);
receiving a request to display one or more service events for the wind turbine ([0109] the same wind turbine is undergoing four different events, including a change in wind speed, a change in pitch, a remote power setpoint change, and a generator outage);
displaying the one or more service events overlaid on the time series data in the one or more data charts ([0116] An instance is a set of time series/event features along with the failure/non-failure of a particular component in a renewable energy asset (e.g., a wind turbine) in a specified time period).
As per claim 10, Visweswariah teaches: receiving a request to analyze data for the wind turbine, the data including one or more of temperature data, signals data, CMS data, and vibration data; ([0101] The SCADA system may also allow remote access to the SCADA system to supervise and monitor any number of wind turbines of any number of wind farms; [0081] one example of the main bearing shaft failure, sensor data regarding temperature, noise, and/or vibration may be taken into account)
receiving one or more of the temperature data, signals data, CMS data, and vibration data; and ([0081] one example of the main bearing shaft failure, sensor data regarding temperature, noise, and/or vibration may be taken into account [0105] SCADA sensors continuously monitor important variables of the wind turbine, environment, and the grid (e.g., temperature of various parts, active/reactive power generation, wind speed, rotation speed, grid frequency, voltage, current)
displaying an analysis of the one or more temperature data, signals data, CMS data, and vibration data ([0105] The sensor data may be a multi-variant time series. FIG. 7 is an example of sensor data from a SCADA system [0081] the sensor data related to temperature, noise, and/or vibration is observed against the background of other sensor data readings, and the sensor dynamic range of each individual sensor or combination of sensors should be recognized [0083] the temperature sensor data may indicate a high temperature warning relative to some high temperature threshold ... The vibration data may be assessed to determine excessive vibration relative to some vibration threshold).
As per claims 12 and 20, these claims recite limitations substantially similar to those addressed by the rejection of claim 1; therefore, the same rejection applies. Additionally, Visweswariah teaches: at least one processor; and memory containing instructions, the instructions being executable by the at least one processor ([0004] An example nontransitory computer readable medium comprises executable instructions. The executable instructions are executable by one or more processors to perform a method).
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 2; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 3; therefore, the same rejection applies.
As per claim 15, this claim recites limitations substantially similar to those addressed by the rejection of claim 4; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 7; therefore, the same rejection applies.
Claim(s) 5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0203157 (Visweswariah); in view of US 2021/0182749 (DT360); in view of US 2018/0320658 (Uptake).
As per claim 5, Visweswariah teaches: …a work order for the gearbox subcomponent or the generator subcomponent of the wind turbine… ([0272] The data preparation module 506 may perform event log-based filtering to discard undesirable SCAD A data that was generated during or immediately before maintenance of the asset and/or components; Fig 27 Scheduled service and Reactive Service).
Although not explicitly taught by Visweswariah, Uptake teaches: receiving a request to initiate a work order for the gearbox subcomponent or the generator subcomponent of the wind turbine; and sending the work order to a work order system ([0068] a work-order system configured to output a request for a mechanic or the like to repair a wind turbine [0080] The wind turbine 300 may include a number of subsystems within or atop of the nacelle 306. As shown in FIG. 3, the hub 304 of the rotor is physically coupled to a gear box 314 via a drive shaft 316, which may take the form of a low-speed drive shaft. The gear box 314 in turn is physically coupled to a generator 318 via a drive shaft 320).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Visweswariah with the aforementioned teachings of Uptake with the motivation of preemptively repairing and maintaining wind turbines (Uptake [0004]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Uptake to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for generation of work requests for wind turbines.
As per claim 16, this claim recites limitations substantially similar to those addressed by the rejection of claim 5; therefore, the same rejection applies.
Claim(s) 6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0203157 (Visweswariah); in view of US 2021/0182749 (DT360); in view of Official Notice.
As per claim 6, Visweswariah teaches: receiving completed service events for the wind turbine for a third historical time period the completed service events including [information]; ([0272] The data preparation module 506 may then identify sensor data generated by the same asset and/or component(s) that generated the log data indicating maintenance ... and subsequently remove sensor data generated at or dming a predetermined period of time ... The predetermined period of time may begin at the time of the first indication from the log data of maintenance ... depending on the type of log indication ( e.g., a first predetermined period of time for maintenance, a second predetermined period of time for a restart [0204] In step 2102, the data extraction module 504 may receive event and alarm data from one or more SCAD A systems used to supervise and monitor any number of wind turbines. The data extraction module 504 may include an input interface to receive detailed event and alarm logs as well as event and alarm metadata. The event and alarm logs may include, but are not limited to, a turbine identifier (e.g., turbineID), event code (e.g., EventCode), event type (e.g., EventType), event start time (e.g., EventStartTime), event end time (e.g., EventEndTime))
receiving open service events for the wind turbine for a fourth future time period; the open service events including date and service detail information; and ([0340] A second stage may be to determine surveillance of assets or components. In this example, potentially failing assets and/or component(s) may be scheduled for surveillance (e.g., by physical inspection))
displaying either the completed service events or the open service events ([Fig 27] [0340] potentially failing assets and/or component(s) may be scheduled for surveillance (e.g., by physical inspection) [0239] … The report may be a maintenance plan or schedule to correct the predicted fault (e.g., preferably before failure and a minimum of power disruption)).
Visweswariah does not teach a completion date and service detail information. However, Official Notice is taken that completion date and service detail information were old and well known before the effective filing date of the invention. One of ordinary skill in the art would have recognized that applying the teachings of the Official Notice to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow access to detailed information related to completed services.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 6; therefore, the same rejection applies.
Claim(s) 8, 11, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0203157 (Visweswariah); in view of US 2021/0182749 (DT360); in view of WO 2018/165669 (GFSI).
As per claim 8, Visweswariah teaches: determining a status of the selected wind turbine; ([0102] The SCADA system may further log data regarding any number of the wind turbine such as failures, health information, performance)
receiving the status of the wind turbine; and ([0104] SCADA system provide important signals for historical and present status of any number of wind turbines (WTs))
displaying … the selected wind turbine and an indication of the status of the selected wind turbine ([Fig 3] [0102] The SCADA system may allow access to the log data to one or more digital devices [0239] … The report may be a maintenance plan or schedule to correct the predicted fault (e.g., preferably before failure and a minimum of power disruption)).
Although not explicitly taught by Visweswariah, GFSI teaches: displaying on a zoomable and scrollable map an icon for the selected wind turbine and an indication of the status of the selected wind turbine ([Fig 8] [Fig 9] [Fig 11] [Page 30] Close-up views can be generated by, e.g., activating the zoom-in icon depicted in the map portion of FIGURE 9. (Zoomed-out views can be generated in a similar manner with the zoom-out icon.) Data related to the turbines of the windfarm is 5 illustrated graphically on the map portion).
One of ordinary skill in the art would have recognized that applying the teachings of GFSI to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the display of wind turbine data on a map.
As per claim 11, Visweswariah teaches: receiving a request to display the multiple wind turbines in one or more wind turbine farms in one or more geographies…; ([0078] As shown in FIG. 3, there may be any number of wind turbines in a wind farm [Fig 3])
displaying … the multiple wind turbines in one or more wind turbine farms in one or more geographies; ([0078] As shown in FIG. 3, there may be any number of wind turbines in a wind farm [Fig 3])
receiving status, performance deviation and/or active alerts for the multiple wind turbines; and ([0204] In step 2102, the data extraction module 504 may receive event and alarm data from one or more SCADA systems used to supervise and monitor any number of wind turbines [0078] Sensors of each wind turbine in a wind farm may generate its own data [0099] The geolocation may indicate the geographic location of a wind turbine or group of wind turbines. Sensor data may be from sensors of electrical assets either individually or in combination (e.g., wind turbines; [0101] the SCADA system may enable control of any number of wind turbines in the wind farm (e.g., clusters of wind turbines, all wind turbines, or one wind turbine). The SCAD A system may provide an overview of relevant parameters of each wind turbine including, for example, temperature, pitch angle, electrical parameters, rotor speed, yaw system, rotor velocity, azimuth angle, nacelle angle, and the like. The SCADA system may also allow remote access to the SCAD A system to supervise and monitor any number of wind turbines of any number of wind farms; [0104] SCADA system provide important signals for historical and present status of any number of wind turbines (WTs))
displaying status, performance deviation and/or active alerts for the multiple wind turbines grouped by the one or more wind turbine farms ([0204] The data extraction module 504 may include an input interface to receive detailed event and alarm logs as well as event and alarm metadata [0006] creating cohort instances based on the wind turbine failure data and wind turbine asset data, each cohort representing a subset of the wind turbines, the subset of the wind turbines including a same type of controller and a similar geographical location, the geographical location of the wind turbines of the subset of wind turbines being within the wind turbine asset data [0102] The SCADA system may further log data regarding any number of the wind turbine such as failures, health information, performance, and the like. The SCADA system may allow access to the log data to one or more digital devices).
Although not explicitly taught by Visweswariah, GFSI teaches: receiving a request to display the multiple wind turbines in one or more wind turbine farms in one or more geographies in a map; displaying a map of the multiple wind turbines in one or more wind turbine farms in one or more geographies; ([Page 30] Close-up views can be generated by, e.g., activating the zoom-in icon depicted in the map portion of FIGURE 9. (Zoomed-out views can be generated in a similar manner with the zoom-out icon.) Data related to the turbines of the windfarm is 5 illustrated graphically on the map portion [Fig. 8][Fig 9]).
One of ordinary skill in the art would have recognized that applying the teachings of GFSI to the system of Visweswariah would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the display of wind turbine data on a map.
As per claim 19, this claim recites limitations substantially similar to those addressed by the rejection of claim 11; therefore, the same rejection applies.
Response to Arguments
Applicant's arguments filed 10/2/2025 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claims improve the functioning of computer-based monitoring systems.
Examiner respectfully disagrees. Additional elements reciting determining … by applying the machine learning model trained … and … alerts being generated by a machine learning model… do not improve the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment (machine learning).
In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). Additional elements in the present claims such as the processor/computer and machine learning model are described at a high level of generality and are only invoked as a tool to perform certain abstract steps (determining health indicators). To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.
Examiner also notes that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. The removal of data related to past failures to highlighting anomalies does not improve computers or technology; instead, these features describe an improvement in the abstract idea.
Features related to temperature rise in the gearbox are directed to data (algorithm utilizes curated data feature including temperature rise in the gearbox…) which falls under the abstract idea. Further, it is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2B. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception is not in itself an inventive concept and does not guarantee eligibility:
With respect to the rejection under 35 USC 103, Applicant argues that the art of record does not disclose the claimed limitations.
Examiner respectfully disagrees. Examiner notes that the Applicant’s arguments are directed to newly amended features; the rejection has been updated to address said amendments. See updated Claim Rejections - 35 USC § 103 above.
Visweswariah discloses the use of a multi-variate anomaly detection algorithm ([0088] … may apply a multi-variate anomaly detection algorithm to sensors that are monitoring operating conditions of any number of renewable assets (e.g., wind turbines and or solar generators). The component failure prediction system 104 may remove data associated with a past, actual failure of the system (e.g. of any number of components and or devices), therefore highlighting subtle anomalies from normal operational conditions that lead to actual failures.)
Conclusion
THIS ACTION IS MADE FINAL. 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 ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625