DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the claims
The argument received on May, 20 2026 has been acknowledged and entered. Claims 14, 22, and 33 are amended. Claims 25-32 are cancelled. Claims 34-37 are newly added. Thus, claims 14-24 and 33-37 are currently pending.
Response to Arguments
Applicant’s amendment filed May, 20 2026 to claim 14 has overcome the objection.
Applicant’s arguments filed May, 20 2026 with respect to the claim rejection under 35 U.S.C. 101 step 1 have been fully considered and are persuasive. Thus, the claim rejection under 35 U.S.C. 101 step 1 has been withdrawn.
Applicant’s arguments filed on May, 20 2026 with respect to claims 14-24 and 33-37 under 35 U.S.C. 101 have been considered but are moot because the new ground of rejection.
Applicant’s arguments filed on May, 20 2026 with respect to claims 14-24 and 33-37 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14-24 and 33-37 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 for inspecting a component of a turbomachine and repairing or returning into service the turbomachine, the method comprising the steps of:
capturing at least one image of the component of the turbomachine using an image-capturing device;
providing metadata from a database about the component, the metadata including a calculated or specified remaining service life of the component, or a nominal or measured wall thickness of the component; and
classifying, by a trained machine learning system, the component into a “serviceable” category or a “non-serviceable” category based on the image captured by the image-capturing device and the provided metadata; and
repairing or returning into service the turbomachine as a function of the classifying.
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 limitation of “classifying, by a trained machine learning system, the component into a "serviceable" category or a "non-serviceable" category based on the image captured by the image-capturing device and the provided metadata” is mathematical calculations (see paras. [0042]-[0043] of instant application) because at training using machine learning system is indicative of mathematical calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claim 33.
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 14: a method for inspecting a component of a turbomachine and repairing or returning into service the turbomachine (preamble); capturing at least one image of the component of the turbomachine using an image-capturing device; providing metadata from a database about the component, the metadata including a calculated or specified remaining service life of the component, or a nominal or measured wall thickness of the component; repairing or returning into service the turbomachine as a function of the classifying and
In Claim 33: a system for inspecting a component of a turbomachine (preamble); an image-capturing device for capturing an image of the component; a database including metadata including a calculated or specified remaining service life of the component, or a nominal or measured wall thickness of the component; and a trained machine learning system configured to receive the image from the image-capturing device and to receive the metadata about the component an image-capturing device for capturing an image of the component;
The additional elements such as a turbomachine, a component of a turbomachine, system, image capturing device, and a trained machine learning system recited at a high-level of generality without descriptions of its specific structure/features to perform the claimed features for producing the mathematical process addressed above (MPEP 2106.05(d)). Further, the additional element of “a method for inspecting a component of a turbomachine and repairing or returning into service the turbomachine” and “a system for inspecting a component of a turbomachine” are preamble statements reciting purpose or intended use (See MPEP 2111.02)(II)). Further, note that steps of “capturing an image of the component; a database including metadata including a calculated or specified remaining service life of the component, or a nominal or measured wall thickness of the component; and receiving the image from the image-capturing device and to receive the metadata about the component” are insignificant (data gathering) extra-solution activity to perform abstract idea (i.e. classifying the component into a “serviceable” category or a “non-serviceable” category based on the data). See MPEP 2106.05(g)). Further, note that the limitation of “ repairing or returning into service the turbomachine as a function of the classifying” is insignificant extra-solution activity (post-solution activity) based on abstract idea (i.e., classifying, by a trained machine learning system, the component) that cannot reasonably integrate the judicial exception into a practical application. Maintenance is insignificant post-solution activity. See MPEP 2106.05(g)). Therefore, none of the additional elements indicate a practical application.
Claim 14 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 classifying, by a trained machine learning system, the component into a “serviceable” category or a “non-serviceable” category based on the image captured by the image-capturing device and the provided metadata. Therefore, the claim 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. Similar limitations comprise the abstract ideas of Claim 33. Therefore, the independent claims 1 and 33 are ineligible.
Regarding claim 15,
The additional element of “the image is a light image, an X-ray or CT image, and the metadata includes a component type, a running time of the component, a number of remaining life cycles, or a repair history” is well-understood, routine, and conventional in the relevant based on the prior art of record (paras. [0059], [0061], [0063] of Campbell; page 5, lines 31-32 of Philipp; paras. [0015], [0026], [0033], [0043], [0055], [0070], [0080] of Zhang). Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record.
Regarding claims 16-24 and 34-37,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations (i.e. machine learning system) and further description of mathematical calculations (i.e. machine learning system). The explanation for the rejection of Claims 16-24 and 34-37 therefore are incorporated herein and applied to Claims 14 and 33. These claims therefore stand rejected for similar reasons as explained in above Claims 14 and 33.
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 14, 16-24, 26, and 33-36 are rejected under 35 U.S.C. under 35 U.S.C. 103 as being unpatentable over Graham et al. (US 2022/0135254 A1,” hereinafter referred to as “Graham”) in view of Campbell et al. (US 2020/0034958 A1,” hereinafter referred to as “Campbell”) (cited in IDS dated August 21, 2023).
Regarding claim 14, Graham teaches a method for inspecting a component of a turbomachine and repairing or returning into service the turbomachine (para. [0042]: servicing operations (including inspection and/or repair) to equipment, such as gas turbine engines), the method comprising the steps of:
capturing at least one image of the component of the turbomachine using an image-capturing device (para. [0042]: servicing operations (including inspection and/or repair) to equipment, such as gas turbine engines…The visual capture device may be configured to observe components being removed from the equipment during servicing operations);
providing metadata from a database about the component (para. [0126]: assess whether conditions of certain components of the equipment 706 are within predetermined limits and ranges; para. [0127]: Visual images and/or video of the inspection can further be analyzed in real-time for damage, defects, and other issues which may appear. Any detected damage, defects, or issues may be tagged with meta data… in addition, any data or information resulting from any one or more tests, analysis, models, and the like may be stored on the data lake, note that the above feature of “data lake” reads on “data base”), the metadata including a calculated or specified remaining service life of the component (para. [0126]: see above; para. [0128]: wear ratings, note that the above feature of “wear rating” reads on “a calculated or specified remaining service life of the component), or a nominal or measured wall thickness of the component (para. [0126]: see above; para. [0128]: The recorded information can relate to any inspection data captured, such as, for example, material thicknesses, wear ratings, damage, and the like); and
repairing or returning into service the turbomachine (paras. [0043]-[0050]: turbomachine ) as a function of the classifying (para. [0136]: Repairs can include the changing of hoses, belts, nozzles, valves, blades, and the like, resurfacing operations, coating operations, cleaning operations, lubricating operations, timing adjustments, and the like; para. [0180]: the input data 2002 can include information associated with the inspection operation and the repair operation. The model 2004 can weigh the various inputs 2002 to determine one or more characteristics of the equipment being serviced, note that the above feature of para. [0136] and input data in para. [180] reads on “repairing or returning into service the turbomachine as a function of the classifying”).
Graham does not specifically teach that classifying, by a trained machine learning system, the component into a “serviceable” category or a “non-serviceable” category based on the image captured by the image-capturing device and provide meta data.
However, Campbell teaches that classifying, by a trained machine learning system, the component into a "serviceable" category or a "non-serviceable" category based on the image captured by the image- capturing device (pars. [0009]-[0016]: machine learning algorithm; para. [0065]: the images are then passed through another set of machine learning algorithms, a classifier would look for any dents, scratches and other unexpected variances to an undamaged car, and determine the likelihood that a variance is present; para. [0066]: assess the severity and/or nature of the damage, note that the above feature of “classifier using machine learning algorithms” and “look for unexpected variances” in para. [0065] and “assess the severity” in para. [0066] reads on “classifying, by a trained machine learning system, the component into a "serviceable" category or a "non-serviceable" category” because severe or serious damage of the object is not serviceable”) and provide meta data (para. [0059]: checking the images EXIF (Exchange Image File Format) data matches the expected criteria (i.e. camera information, geolocation (i.e. GPS data) or other suitable metadata)).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 classifying, by a trained machine learning system, the component such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 16, Gram in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teaches that the machine learning system classifies the components classified as "non-serviceable" into either a "repairable" category or a "non-repairable" category (para. [0066]: separate collection of machine learning algorithms, e.g. convolutional neural networks, which assess the severity and/or nature of the damage; para. [0070]: the data output generated by the various algorithms is then passed to the system to generated a complete report on the damages, cost of repair and/or replacement, as well as, any fraudulent activities, note that the above feature of “machine learning algorithms” and “assess the severity” in para. [0066] and “cost of repair and/or replacement” in para. [0070] reads on “the machine learning system classifies the components classified as "non-serviceable" into either a "repairable" category or a "non-repairable" category” because some serious or sever damage need replacement instead of repair).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 17, Gram in view of Campbell teaches all the limitation of claim 16, in addition, Campbell teaches that the machine learning system assigns a probability of successful repair to the components classified as "repairable" (para. [0016]: using machine learning algorithms; para. [0052]: providing an estimate for potential repair time based on automated decision making, note that “machine learning algorithm” in para. [0016] and “estimate for potential repair time” reads on “the machine learning system assigns a probability of successful repair to the components classified as "repairable”).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 18, Gram in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teachesthat the machine learning system (para. [0016]: machine learning algorithms) includes a neural network or a support vector machine (para. [0022]: artificial neural networks; paras. [0063]-[0065]: convolutional neural networks).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 19, Gram in view of Campbell teaches all the limitation of claim 18, in addition, Campbell teaches that the neural network is a deep neural network (para. [0022]: any combination of Deep Learning algorithms, artificial neural networks, statistical modelling), a convolutional neural network (paras. [0063]-[0065]: convolutional neural networks).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 neural network such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 20, Gram in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teaches that the machine learning system (para. [0063]: machine learning algorithms, e.g. convolutional neural networks) is configured to identify or locate defects in the at least one image, and to take the identified or located defects into account in the classification of the component (para. [0069]: the identified locations any damage is stored allowing the user to later visually mark the damages in various formats).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 21, Gram in view of Campbell teaches all the limitation of claim 20, in addition, Campbell teaches that the defects are cracks or pores and a type (para. [0056]: the identified damage is structural, non-structural or a complete/severe all-round damage, the extent of damage that is detected (e.g. scratch, dent etc.), note that the above feature of structural reads on “cracks or pores”), position (para. [0046]: obtain location of identified damage; para. [0069]: the identified locations any damage is stored), number or size of the identified defects is taken into account in the classification (para. [0050]: damaged areas location within image data).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 defects such as are described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 22, Gram in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teaches that the metadata used includes at least remaining life cycles of the component or data of the operator of the components or geographical data or environmental data (para. [0059] Checking the images EXIF (Exchange Image File Format) data matches the expected criteria (i.e. camera information, geolocation (i.e. GPS data) or other suitable metadata)).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 metadata used such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 23, Gram in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teaches that the machine learning system (para. [0016]: machine learning algorithms) is configured to autonomously control the image-capturing device (para. [0062]: automatically assess and process the damaged object), after analysis of the at least one image, to capture at least one further image of the component with a varied imaging parameter if a classification criterion cannot be satisfied based on the at least one initial image (para. [0058]: automatically detect any image manipulation, for example, the image originator has attempted to modify/manipulate any images; para. [0059]: checking the images EXIF (Exchange Image File Format) data matches the expected criteria, note that the above feature of “automatically detect any image manipulation” in para. [0058] and “checking the images EXIF” in para. [0059] reads on “analysis of the at least one image, to capture at least one further image of the component with a varied imaging parameter if a classification criterion cannot be satisfied based on the at least one initial image”).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 24, Gram in view of Campbell teaches all the limitation of claim 23, in addition, Campbell teaches that the varied imaging parameter is a varied imaging angle (para. [0063]: the image view angle and perspective is classified utilising a collection of different convolutional neural networks. In case a set of a plurality of images is provided, the image(s) with the most suitable viewing angles is (are) selected to provide the system with a maximum of information of the object(s)).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 varied imaging parameter such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 33, Graham teaches a system for inspecting a component of a turbomachine (para. [0042]: servicing operations (including inspection and/or repair) to equipment, such as gas turbine engines), the system comprising:
an image-capturing device for capturing an image of the component (para. [0042]: servicing operations (including inspection and/or repair) to equipment, such as gas turbine engines…The visual capture device may be configured to observe components being removed from the equipment during servicing operations);
a database including metadata including a calculated (para. [0126]: assess whether conditions of certain components of the equipment 706 are within predetermined limits and ranges; para. [0127]: Visual images and/or video of the inspection can further be analyzed in real-time for damage, defects, and other issues which may appear. Any detected damage, defects, or issues may be tagged with meta data… in addition, any data or information resulting from any one or more tests, analysis, models, and the like may be stored on the data lake, note that the above feature of “data lake” reads on “data base”) or specified remaining service life of the component (para. [0126]: see above; para. [0128]: wear ratings, note that the above feature of “wear rating” reads on “a calculated or specified remaining service life of the component), or a nominal or measured wall thickness of the component (para. [0126]: see above; para. [0128]: The recorded information can relate to any inspection data captured, such as, for example, material thicknesses, wear ratings, damage, and the like); and
a trained machine learning system configured to receive the image from the image-capturing device and to receive the metadata about the component (para. [0127]: Visual images and/or video of the inspection can further be analyzed in real-time for damage, defects, and other issues which may appear. Any detected damage, defects, or issues may be tagged with meta data; para. [0143]: the one or more computing devices 328 and/or 330 may utilize a machine learning tool trained to identify the presence and/or extent of damage to the thermal barrier coating, environmental barrier coating, the like, or other component within an interior of an engine).
Graham does not specifically teach being trained to classify the component into a “serviceable” category or a “non-serviceable”
However Campbell teaches being trained to classify the component into a “serviceable” category or a “non-serviceable” (pars. [0009]-[0016]: machine learning algorithm; para. [0065]: the images are then passed through another set of machine learning algorithms, a classifier would look for any dents, scratches and other unexpected variances to an undamaged car, and determine the likelihood that a variance is present; para. [0066]: assess the severity and/or nature of the damage, note that the above feature of “classifier using machine learning algorithms” and “look for unexpected variances” in para. [0065] and “assess the severity” in para. [0066] reads on “classifying, by a trained machine learning system, the component into a "serviceable" category or a "non-serviceable" category” because severe or serious damage of the object is not serviceable”).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 being trained to classify the component into a “serviceable” category or a “non-serviceable” such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding claim 34, Graham in view of Campbell teaches all the limitation of claim 21, in addition, Campbell teaches that the machine learning system (pars. [0009]-[0016], [0064], [0066]: machine learning algorithm) is further configured to output a marked image, the marked image including the image and markings of detected defects (para. [0069]: The identified locations any damage is stored allowing the user to later visually mark the damages in various formats. For example, the detected damages (i.e. variances) may be visually marked using heat maps, shading on the images or encircling, or any other visual form of highlighting the damaged areas within the image).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Claim 35, Graham in view of Campbell teaches all the limitation of claim 21, in addition, Campbell teaches that the markings of detected defects include colored elements corresponding to shapes of the detected defects (para. [0022]: Computer Vision algorithms. This provides the advantage of an intelligent and highly adaptable system/method capable of automatic object/damage recognition/interpretation/classification; para. [0023]: the visual marker may comprise any one or any combination of shading, encircling, highlighting and color-coded mapping).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 machine learning system such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Regarding Claim 36, Graham in view of Campbell teaches all the limitation of claim 17, in addition, Graham teaches that the probability of success is determined based on an inspection database, the inspection database including previous inspection results and final results of previous repairs (para. [0128]: The information from the n+1 servicing operation can be synced with information from the nth servicing operation so as to provide a service history of the equipment. The servicing history can be used to autonomously inform aspects of future workscopes of the equipment 706… saved inspection and/or repair information can be used by the one or more computing devices 328 and/or 330 to train processing elements to perform or upgrade inspections and repairs autonomously).
Claim 15 is rejected under 35 U.S.C. under 35 U.S.C. 103 as being unpatentable over Graham in view of Campbell and Philipp et al. (DE 102016200779 A1, hereinafter referred to as “Philipp”).
Regarding claim 15, Graham in view of Campbell teaches all the limitation of claim 14, in addition, Campbell teaches that the image is a light image and the metadata (para. [0059]: metadata; para. [0061]: the present invention is capable of automatically processing images captured by user of any technical and/or photographing skill, note that the above feature of “ metadata” in para. [0059] and “any technical and/or photographing” in para. [0061] reads on “the image is a light image, an X-ray or CT image, and the metadata”) includes a component type (para. [0063]: determine the main component (object) is what is expected), a running time of the component, a number of remaining life cycles, or a repair history (para. [0063]: collection of algorithms to classify, whether or not, any of the images have been manipulated (using EXIF data) and, whether or not, the claim is fraudulent (i.e. by checking the vehicles insurance claim history, note that “checking the vehicles insurance claim history” reads on “a running time of the component, a number of remaining life cycles, or a repair history”).
Graham and Campbell are both considered to be analogous to the claimed invention because they are in the same filed of detecting/analysing/assessing damage to an object (or component) using machine learning algorithm. 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 image such as is described in Campbell into Graham, in order to generate a predictive analysis to repair and/or replace said at least one damaged object, utilizing a machine learning algorithm and said classified extent of damage of the detected said at least one damaged area. (Campbell, para. [0015]).
Graham and Campbell do not specifically teach an X-ray or CT image.
However, Philipp teaches an X-ray or CT image (page 5, lines 31-32: during its maintenance, overhaul or repair, that is not during or immediately following its manufacture, with the aid of a computed tomography device (CT) is carried out; page 5, lines 41-42: in additional set-up effort is avoided if in the context of the maintenance of the component and an additional X-ray inspection is required).
Graham and Philipp are both considered to be analogous to the claimed invention because they are in the same filed of investigation of component (or object). 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 an X-ray or CT image such as is described in Philipp into Graham, in order to permit an improved assessment of the quality of the component (Philipp, page 5, line 21).
Claim 37 is rejected under 35 U.S.C. under 35 U.S.C. 103 as being unpatentable over Graham in view of Campbell and Sriraman (US 2019/0287238 A1,” hereinafter referred to as “Sriraman”).
Regarding Claim 37, Graham in view of Campbell teaches all the limitation of claim 17.
Graham and Campbell do not specifically teach that the machine learning system is configured to identify or locate defects in the at least one image, the probability of success being determined based on lengths of the defects, widths of the defects, diameters of the defects, or a number of defects.
However, Sriramen teaches that the machine learning system is configured to identify or locate defects in the at least one image, the probability of success being determined based on lengths of the defects, widths of the defects, diameters of the defects, or a number of defects (para. [0045]: Classifying as the final defect type can use a machine learning algorithm. The machine learning algorithm can be trained with properties of the defect and the final defect type. The properties can include one or more critical dimension attributes. While this may include contrast and topography attributes, this also can include pattern width, length, diameter, area, angle, roughness, or edge placement errors. The critical dimension attribute can classify defects based on severity).
Graham and Sriramen are both considered to be analogous to the claimed invention because they are in the same filed of detecting defect in image damage using machine learning algorithm. 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 machine learning system such as is described in Sriramen into Graham, in order to allow critical dimension uniformity parameters associated with the defect type to be retrieved from an electronic data storage unit using the processor (Sriraman, para. [0009]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Graham et al. (US 11,935,290 B2) teaches Systems and methods of servicing engines, an exemplary method of servicing an engine, the method including receiving, by one or more computing devices, information corresponding to one or more components of the engine; determining, by the one or more computing devices, a location of the one or more components of the engine with respect to a location of an augmented reality device; and presenting, in a current field of view display of the augmented reality device, at least a portion of the information corresponding to the one or more components of the engine
Hadewig et al. (US 2019/0232371 A1) teaches a layer-by-layer manufacturing method for the additive production of a region of a component, in particular of a turbomachine.
Aschermann et al. (US 2017/0226860 A1) teaches a repair method for guide blades of a gas turbine. The method comprises: providing at least one guide blade to be maintained; capturing the actual geometry of the guide blade to be maintained with application of at least one measuring method; comparing the actual geometry captured by the contactless measuring method to a predetermined desired geometry for a corresponding guide blade type.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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