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 .
Information Disclosure Statement
The information disclosure statement (IDSs) submitted on 03/20/2024 and 10/17/2025 were in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Specifically, representative Claim 1 recites:
A method of estimating a remaining service life of a component of an aircraft power plant, the method comprising, comprising:
while the component is unused in an operation of the aircraft power plant: inducing an induced vibration in the component; and acquiring a measured response to the induced vibration in the component;
using a computer-implemented trained model, determining an estimated remaining service life for the component based on the measured response, the computer-implemented trained model having been trained using machine learning and historical data associating previous responses to induced vibrations with previous remaining service lives; and
generating an output indicative of the estimated remaining service life of the component.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, the limitations of “using a computer-implemented trained model, determining an estimated remaining service life for the component based on the measured response, the computer-implemented trained model having been trained using machine learning and historical data associating previous responses to induced vibrations with previous remaining service lives” is mathematical calculations (see paras. [0076]: model 34, 134 may include a regression algorithm that automatically assigns a numerical value that is within a range, para. [0076]: model 34, 134 may be generated using one or more neural networks) or mental processes. A person can compare the vibration data from the measured response and compare it to past data to assign a type of data and degree of severity. The further limitation of “when the health condition of the component is indicative of the component being suitable for service, installing the component in the aircraft power plant” in claim 15 merely describes the component to be performed by abstract idea (i.e., relating a characteristic of the response to a health condition of the component using a computer-implemented trained model). Therefore, this limitation is merely a part of abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations and/or human mind, then it falls within “Mathematical concepts” and/or “Mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 14, 15, and 18.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: a method of estimating a remaining service life of a component of an aircraft power plant (preamble); while the component is unused in an operation of the aircraft power plant: inducing an induced vibration in the component; and acquiring a measured response to the induced vibration in the component; generating an output indicative of the estimated remaining service life of the component.
In Claim 15: a method of manufacturing an aircraft power plant (preamble); manufacturing a component of the aircraft power plant; while the component is uninstalled from the aircraft power plant: inducing a vibration in the component; and acquiring a response to the vibration induced in the component; and
In Claim 18: a system for estimating a remaining service life of a component of an aircraft power plant (preamble); an inducer operable to cause an induced vibration in the component; a sensor operable to acquire a response to the induced vibration in the component; one or more data processors; and non-transitory machine-readable memory storing: a trained model trained using machine learning and historical data associating previous responses to induced vibrations with previous remaining service lives for the component; generating an output indicative of the estimated remaining service life of the component.
The additional elements such as the aircraft power plant, component of an aircraft power plant, a system, a sensor, an inducer, a non-transitory computer-readable storage medium, and one or more data processors are recited at a high-level of generality without descriptions of its specific structure/features to perform the claimed features for producing the mathematical or mental processes addressed above (MPEP 2106.05(d)). Further, the additional element of “a system for estimating a remaining service life of a component of an aircraft power plant,” “a method of manufacturing an aircraft power plant,” and “a system for estimating a remaining service life of a component of an aircraft power plant” are preamble statements reciting purpose or intended use (See MPEP 2111.02)(II)). Further, the limitation of “manufacturing a component of the aircraft power plant “ is a particular technological environment or field of use (See MPEP 2106.05(h)). Further, note that steps of “while the component is uninstalled from the aircraft power plant: inducing a vibration in the component; and acquiring a response to the vibration induced in the component” are insignificant (gathering data) extra-solution activity to perform abstract idea that is mathematical calculations or mental processes (i.e. using a computer-implemented trained model, determining an estimated remaining service life for the component) (MPEP 2106.05(g)). The limitations of inducing a vibration in the component and acquiring a response to the vibration induced in the component are merely a routine data gathering although “inducing an induced vibration in the component” is real world. Further, the limitation of “generating an output indicative of the estimated remaining service life of the component” is an insignificant post-solution activity. Merely “notifying” a result (i.e., generating an output indicative of the estimated remaining service life of the component) to prove that such a feature is insufficient extra solution activity (see MPEP 2106.05(g)). Therefore, independent claims 1, 8, and 15 are not patent eligible.
Claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts, for example, how and or with what to estimate a remaining service life of a component of an aircraft power plant. Therefore, the claims have no significance more beyond the abstract idea. Further, an abstract idea itself is just that, abstract, and whether such feature is or is not significant does not preclude it from being considered abstract. An abstract idea by itself, whether it or not it has a benefit, does not reasonably overcome a 101 rejection because it is still an abstract idea. Therefore, the above advantages relate to abstract idea limitations which are not considered. The Improvements in the abstract idea are not qualified as improvements indicating a practical application. The pending claims are not patent eligible since a claim for a new abstract idea is still an abstract idea (see MPEP 2106.05(a).I) and an improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a).II and MPEP 2106.05(a).II: Examples that the courts have indicated may not be sufficient to show an improvement to technology include: iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48)). This is just a processor running mathematics or mental processes using machine learning algorithm. Similar limitations comprise the abstract ideas of Claims 15 and 18. Therefore, the independent claims 1, 15, and 18 are ineligible.
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 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.
Claims 1-2, 4-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balkowski et al. (DE 102018213475 A1, hereinafter referred to as “Balkowski) (cited in IDS dated March 20, 2024) in view of Kumar et al. (“Latest innovations in the field of condition-based maintenance of rotatory machinery: a review”, measurement science and Technology, November 30, 2021, Vol. 35. IOP Publishing Ltd, hereinafter referred to as “Kumar”) (cited in IDS dated March 20, 2024).
Regarding claim 1, Balkowski teaches a method of estimating a remaining service life of a component of an aircraft power plant (Fig. 4 and claim1; page 2, line 9: In steam turbines and also in compressors and in gas turbines), the method comprising:
while the component is unused in an operation of the aircraft power plant (page 2, line 9: In steam turbines and also in compressors and in gas turbines): inducing an induced vibration in the component; and acquiring a measured response to the induced vibration in the component (Fig. 4 and claim 1: Method for carrying out a sound test on a multicomponent component (100), in particular a blade assembly (100), in which a microphone is used beforehand either by direct mechanical excitation of a multicomponent component (100) in the initial state, in particular a new multicomponent component (100) (20) the airborne sound generated in this way is measured, and relevant acoustic parameters of the airborne sound, in particular frequency images (1) and / or frequency profiles (1) and / or decay behavior (4) or other acoustic characteristics are determined, or the relevant acoustic parameters such as frequency images ( 1) and / or, frequency profiles and / or decay behavior (4) are calculated numerically, - these being or have been stored in a database and carrying out an excitation (17), in particular a mechanical excitation (17), of a component (100) after use to generate body vibrations in the component and the resulting air pressure all, - measurement of airborne noise using a spaced-apart microphone (20), Determination of the relevant acoustic parameters, in particular frequency images (2) and / or frequency profiles (2) and / or decay behavior (7), - these with the frequency image (1) and / or frequency profiles and / or decay behavior (4) of the component (100) in the initial state, which is stored in the database, is compared and deviations are detected and in particular also evaluated, note that the above feature of “method for carrying out a sound test on a multicomponent component (100), in particular a blade assembly (100), in which a microphone is used beforehand either by direct mechanical excitation of a multicomponent component (100) in the initial state” and “mechanical excitation (17), of a component (100) after use to generate body vibrations in the component” in claim 1 reads on “while the component is unused”) and acquiring a measured response to the induced vibration (Fig. 2, 17:hammer) in the component (page 2, lines 9-12: performance. The individual components are currently being dismantled in order to inspect the shovel assembly. The assessment takes place by means of a hammer blow on the bandage and subjective evaluation using a sound image. The sound image results from the acoustic processing by the human ear).
Balkowski does not specifically teach using a computer-implemented trained model, determining an estimated remaining service life for the component based on the measured response, the computer-implemented trained model having been trained using machine learning and historical data associating previous responses to induced vibrations with previous remaining service lives and generating an output indicative of the estimated remaining service life of the component.
However, Kumar teaches using a computer-implemented trained model, determining an estimated remaining service life for the component based on the measured response, the computer-implemented trained model having been trained using machine learning and historical data associating previous responses to induced vibrations with previous remaining service lives (page 7 right column, line 8-page 8, left column, line 2: AI has been playing an important role in CBM or CM. AI can be described as a performance indicator of monitoring the health of equipment, systems, and processes to detect potential problems before they become failures. By leveraging machine learning algorithms, AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies, allowing for predictive maintenance and improving equipment reliability and uptime. Deep learning techniques, as subsets of machine learning, use artificial neural networks (NNs) to model and solve complex problems. These techniques are characterized by their ability to automatically learn hierarchical representations of data, which can be employed for accurate predictions, classifications, or generate new data, note that the above feature of “predictive maintenance” and “accurate predictions” reads on “determining an estimated remaining service life for the component”) and generating an output indicative of the estimated remaining service life of the component (page 7 right column, line 8-page 8, left column, line 2: by leveraging machine learning algorithms, AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies, allowing for predictive maintenance and improving equipment reliability and uptime…these techniques are characterized by their ability to automatically learn hierarchical representations of data, which can be employed for accurate predictions, classifications, or generate new data).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the using a computer-implemented trained model such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 2, Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Kumar teaches determining the estimated remaining service life for the component includes: identifying an internal defect present in the component based on the measured response (page 7 right column, line 8-page 8, left column, line 2: AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies); and relating the internal defect to the estimated remaining service life (page 7 right column, line 8-page 8, left column, line 2: AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies, allowing for predictive maintenance and improving equipment reliability and uptime. Deep learning techniques, as subsets of machine learning, use artificial neural networks (NNs) to model and solve complex problems. These techniques are characterized by their ability to automatically learn hierarchical representations of data, which can be employed for accurate predictions, classifications, or generate new data).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determining the estimated remaining service life for the component such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 5. Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Kumar teaches determining the estimated remaining service life for the component includes: identifying a characteristic in the measured response (page 7 right column, line 8-page 8, left column, line 2: AI has been playing an important role in CBM or CM. AI can be described as a performance indicator of monitoring the health of equipment, systems, and processes to detect potential problems before they become failures. By leveraging machine learning algorithms, AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies); and determining the estimated remaining service life for the component based on the characteristic (page 7 right column, line 8-page 8, left column, line 2: allowing for predictive maintenance and improving equipment reliability and uptime. Deep learning techniques, as subsets of machine learning,
use artificial neural networks (NNs) to model and solve complex problems. These techniques are characterized by their ability to automatically learn hierarchical representations of data, which can be employed for accurate predictions, classifications, or generate new data).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determining the estimated remaining service life for the component such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 6, Balkowski in view of Kumar teaches all the limitation of claim 5, in addition, Kumar teaches that the characteristic includes a resonant frequency (page 6 left column line 1-pag 7 right column line 7: modal analysis for defect identification and natural frequency).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the characteristic including a resonant frequency such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regrading claim 7, Balkowski in view of Kumar teaches all the limitation of claim 5, in addition, Balkowski teaches that the characteristic includes a mode shape (page 2, line 18: Fig. s 1 . 2 and 3 show samples of the measurements using the sound sample; page 4, liens 36-38: the pattern recognition recognizes the deviation from the target condition and assigns the blade rows as a component to a further classification such as "acceptable" or "to be replaced". These classifications are determined beforehand based on preliminary examinations and existing measurements. The 1 . 2 . 3 represent exemplary patterns that are created from the recordings of airborne sound).
Regarding claim 8, Balkowski in view of Kumar teaches all the limitation of claim 5, in addition, Kumar teaches that the characteristic includes a damping coefficient (page 6 left column line 1-pag 6 right column line 4: The modal analysis can be used to find out how much energy moves through a material as it vibrates. This gives you an idea of how much work has been done on the material by this vibration, which means we can figure out if there will be any damage to it if something else happens to it.. The object has some point where it will vibrate at its natural frequency. called an Eigen-frequency. The natural frequencies of different types of structures can vary widely: for example, a pipe has a very low natural frequency compared to a bridge or an arch. To determine how much support a structure needs; we can calculate the Eigen-frequencies of each part of the structure and take into account how much is the participation of each mass, note that pipe has a low Q factor compared bridge. high Q means high quality bearing (i.e. low damping coefficient)).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the characteristic including a damping coefficient such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 9, Balkowski in view of Kumar teaches all the limitation of claim 5, in addition, Kumar teaches that the characteristic includes a Q factor (page 6 left column line 1-pag 6 right column line 4: The modal analysis can be used to find out how much energy moves through a material as it vibrates. This gives you an idea of how much work has been done on the material by this vibration, which means we can figure out if there will be any damage to it if something else happens to it.. The object has some point where it will vibrate at its natural frequency. called an Eigen-frequency. The natural frequencies of different types of structures can vary widely: for example, a pipe has a very low natural frequency compared to a bridge or an arch. To determine how much support a structure needs; we can calculate the Eigen-frequencies of each part of the structure and take into account how much is the participation of each mass, note that pipe has a low Q factor compared bridge. high Q means high quality bearing (i.e. low damping coefficient)).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the characteristic including a Q factor such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 10, Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Balkowski teaches that the induced vibration is different from an expected in-use condition experienced by the component during the operation of the aircraft power plant (page 2, lines 9-12: in steam turbines and also in compressors and in gas turbines, individual rows of blades are connected by means of a blade root and shroud. This creates a firm bond that is insensitive to vibration excitation from the flow medium. The dressing can loosen during operation, which can result in blade damage, damage to adjacent components and loss of performance, note that the above feature of “the dressing can loosen during operation, which can result in blade damage, damage to adjacent components and loss of performance” reads on “the induced vibration is different from an expected in-use condition experienced by the component during the operation of the aircraft power plant”).
Regarding claim 11, Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Balkowski teaches that inducing the induced vibration and acquiring the measured response are performed before an initial use of the component in the operation of the aircraft power plant (Fig. 4 and claim 1: Method for carrying out a sound test on a multicomponent component (100), in particular a blade assembly (100), in which a microphone is used beforehand either by direct mechanical excitation of a multicomponent component (100) in the initial state, note that the above feature of “direct mechanical excitation of a multicomponent component (100) in the initial state” reads on “inducing the induced vibration and acquiring the measured response are performed before an initial use of the component in the operation of the aircraft power plant”).
Regarding claim 12, Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Balkowski teaches that inducing the induced vibration and acquiring the measured response are performed after the component has been used in the operation of the aircraft power plant (Fig. 4 and claim 1: carrying out an excitation (17), in particular a mechanical excitation (17), of a component (100) after use to generate body vibrations in the component).
Regarding claim 13, Balkowski in view of Kumar teaches all the limitation of claim 1, in addition, Balkowski teaches that inducing the induced vibration includes striking the component (Fig, 4, 17: hammer).
Regarding claim 14, it is a computer program product type claim and has similar limitation as of claim 1 above. The additional limitations of the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable/executable by a computer, processor or logic circuit to perform the method as defined in claim 1 (Kumar, Fig. 7: laptop computer), taught by Kumar.
Regarding claim 18, it is system type claim and has similar limitation as of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above.
Regarding claim 19, Balkowski in view of Kumar teaches all the limitation of claim 18, in addition, Balkowski teaches that the inducer is a hammer operable to strike the component (Fig. 1, 17; page 3, lines 25-26: in 4 the execution of the sound test is also shown by means of mechanical excitation, e.g. B. a hammer 17 which can be checked manually or by a pulse generator and carried out directly).
Regarding claim 20, Balkowski in view of Kumar teaches all the limitation of claim 18, in addition, Balkowski teaches that the inducer is a transducer operable to induce a sound wave into the component (Fig. 1, pulse generator and carried out directly; page 3, lines 25-26: in 4 the execution of the sound test is also shown by means of mechanical excitation, e.g. B. a hammer 17 which can be checked manually or by a pulse generator and carried out directly).
Claims 3 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Balkowski in view of Kumar and Xu (US 2016/0341045 A1, hereinafter referred to as “Xu”).
Regarding claim 3, Balkowski in view of Kumar teaches all the limitation of claim 2. Balkowski and Kumar do not specifically teach that the component is a metallic component made by additive manufacturing.
However, Xu teaches that the component is a metallic component made by additive manufacturing (para. [0017]: A method of manufacturing a component according to another disclosed non-limiting embodiment of the present disclosure includes additively manufacturing the component of a metal material).
Balkowski and Xu are both considered to be analogous art to the claimed invention because they are in the similar filed of component for a turbine engine. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the manufacturing the component of the aircraft power plant such as is described in Xu into Balkowski, in order to include additively manufacturing the component of a metal material; additively manufacturing a core at least partially within the component; at least partially encasing the additively manufactured component and additively manufactured core within a shell; (Kumar, abstract).
Regarding claim 15, Balkowski in view of Kumar teaches a method of manufacturing an aircraft power plant (Balkowski, page 2, line 9: In steam turbines and also in compressors and in gas turbines), the method comprising:
while the component is uninstalled from the aircraft power plant: inducing a vibration in the component (Balkowski, page 2, lines 9-12: performance. The individual components are currently being dismantled in order to inspect the shovel assembly. The assessment takes place by means of a hammer blow on the bandage and subjective evaluation using a sound image. The sound image results from the acoustic processing by the human ear ); and
acquiring a response to the vibration induced in the component (Balkowski, page 2, lines 9-12: performance. The individual components are currently being dismantled in order to inspect the shovel assembly. The assessment takes place by means of a hammer blow on the bandage and subjective evaluation using a sound image. The sound image results from the acoustic processing by the human ear);
using a computer-implemented trained model, relating a characteristic of the response to a health condition of the component (Kumar, page 7 right column, line 8-page 8, left column, line 2: see claim 1 above); and
when the health condition of the component is indicative of the component being suitable for service, installing the component in the aircraft power plant (Balkowski, page 2, lines 21-28: the aim is to provide the sound pattern of a new component or a technically approved component, in particular a row of blades, for pattern recognition. To do this, the sound pattern must first be assigned to a row of blades….These blueprints are fed to a pattern recognition and assigned as a "healthy" row of blades).
Balkowski and Kumar do not specifically teach that manufacturing a component of the aircraft power plant.
However, Xu teaches that manufacturing a component of the aircraft power plant (para. [0017]: a method of manufacturing a component according to another disclosed non-limiting embodiment of the present disclosure includes additively manufacturing the component of a metal material).
Balkowski and Xu are both considered to be analogous art to the claimed invention because they are in the similar filed of component for a turbine engine. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the manufacturing the component of the aircraft power plant such as is described in Xu into Balkowski, in order to include additively manufacturing the component of a metal material; additively manufacturing a core at least partially within the component; at least partially encasing the additively manufactured component and additively manufactured core within a shell; (Kumar, abstract).
Regarding claim 16, Balkowski in view of Kumar and Xu teaches all the limitation of claim 15, in addition, Kumar teaches training the computer-implemented trained model using machine learning and historical data relating a previous characteristic to a previous health condition (page 7 right column, line 8-page 8, left column, line 2: AI has been playing an important role in CBM or CM. AI can be described as a performance indicator of monitoring the health of equipment, systems, and processes to detect potential problems before they become failures. By leveraging machine learning algorithms, AI can analyse large volumes of data to identify patterns and anomalies that may indicate faults or inefficiencies, allowing for predictive maintenance and improving equipment reliability and uptime. Deep learning techniques, as subsets of machine learning, use artificial neural networks (NNs) to model and solve complex problems. These techniques are characterized by their ability to automatically learn hierarchical representations of data, which can be employed for accurate predictions, classifications, or generate new data).
Balkowski and Kumar are both considered to be analogous art to the claimed invention because they are in the similar filed of health monitoring in rotatory machinery. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the training the computer-implemented trained model such as is described in Kumar into Balkowski, in order to apply artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life (Kumar, abstract).
Regarding claim 17, Balkowski in view of Kumar and Xu teaches all the limitation of claim 16, in addition, Balkowski teaches relating a characteristic in the response to the health condition of the component includes: identifying an internal defect present in the component based on the characteristic (page 2, lines 9-12: in steam turbines and also in compressors and in gas turbines, individual rows of blades are connected by means of a blade root and shroud. This creates a firm bond that is insensitive to vibration excitation from the flow medium. The dressing can loosen during operation, which can result in blade damage, damage to adjacent components and loss of performance. The individual components are currently being dismantled in order to inspect the shovel assembly. The assessment takes place by means of a hammer blow on the bandage and subjective evaluation using a sound image. The sound image results from the acoustic processing by the human ear); and relating the internal defect to the health condition (page 2, lines 9-12: see above; page 2, lines 21-28: the aim is to provide the sound pattern of a new component or a technically approved component, in particular a row of blades, for pattern recognition. To do this, the sound pattern must first be assigned to a row of blades….These blueprints are fed to a pattern recognition and assigned as a "healthy" row of blades).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Balkowski in view of Kumar and Glowacki (FR 2682992 A1, hereinafter referred to as “Glowacki”).
Regarding claim 4, Balkowski in view of Kumar teaches all the limitation of claim 2. Balkowski and Kumar do not specifically teach that the component is made from a fiber-reinforced composite material and the internal defect includes a delamination.
However, Glowacki teaches that the component is made from a fiber-reinforced composite material and the internal defect includes a delamination (page 4, lines 20-22: It is also known to produce the fixed or mobile blades of a turbomachine by using composite materials comprising reinforcing fibers arranged in an organic resin of the epoxy or polyimide type. These solutions are not however entirely satisfactory and have the drawbacks of a lack of resistance to erosion and too great a brittleness. Material decohesions and delamination are thus observed following impacts).
Balkowski and Glowacki are both considered to be analogous art to the claimed invention because they are in the similar filed of turbines of for a turbine machine. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the fiber-reinforced composite material and the internal defect including the delamination such as is described in Xu into Balkowski, in order to comprise reinforcing fibers arranged in an organic resin of the epoxy or polyimide type. These solutions are not however entirely satisfactory and have the drawbacks of a lack of resistance to erosion and too great a brittleness (Glowacki, page 5, lines 19-21).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Braswell et al. (US 7,957,851 B2) teaches a system and method is provided for monitoring, collecting, storing, transmitting, calculating and uploading to a secure web server real-time balancing solutions for at least the N1 Low Pressure Compressor portion of a jet engine. The method comprises collecting a series of engine vibration data strings and transmitting a data string set to a ground stations for conditioning, formatting and forwarding to a source for calculating and providing fan balance solutions to a secure server wherein the data strings comprise at least phase angle of imbalance data, N1 RPM and the magnitude of the N1 vibration.
Vian et al. (US 7,321,809 B2) teaches methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network inverse model. The neural network inverse model establishes a relationship between the vibrational data and an unbalance condition of the engine, and outputs diagnostic information indicating the unbalance condition of the engine.
Stefan (DE 102006048791 A1) teaches a method for testing the quality of workpieces or machine parts which are exposed to high forces or high rotational accelerations, for example in aircraft engines, power turbines or the like, by means of sound analysis, comprising the following steps: - Excitation of the workpiece or machine part to be tested (test object) to mechanical Vibrations, - detection of the vibrations of the test object, - transmission of the detected vibrations to a signal processing device, - comparison and evaluation of the detected vibrations with previously detected vibrations of the test object or with the vibrations of a reference object and, if necessary, evaluation of the acquired data or comparison.
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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