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 .
Response to Amendment
The Amendment filed on April 21st, 2026 has been entered. Claims 1–22 are currently pending. No claims have been canceled. Claims 1 and 12 have been amended.
Response to Arguments
Applicant's arguments filed 4/21/2026 have been fully considered but are not persuasive with respect to the rejection under 35 U.S.C. §103. Applicant’s arguments, see pages 5–8 of the Remarks, state that Mahadevappa only evaluates wire/metal ropes and does not disclose evaluating ropes having fibers. Applicant further argues that neither Mahadevappa nor Kuwertz teaches a knowledge base trained with data comprising image segments having previously classified damage levels. Finally, Applicant argues that Kuwertz is directed to oilfield tools, not ropes, and combining the references would change the principle of operation of Mahadevappa's system. The Examiner respectfully disagrees, as set forth below:
Regarding Applicant's argument that Mahadevappa is directed to wire ropes rather than a "rope having a plurality of fibers", this argument is moot, as provided by Applicant's disclosure in [0021], Plaia et al (US 2021/0190756 A1, 2021) expressly defines “fiber” as non-metal natural and synthetic fiber structures. Thus, Plaia confirms that a rope “having a plurality of fibers” is an ordinary rope construction and provides explicit context that such fiber-rope constructions are within the scope of rope types routinely evaluated for expected/remaining life.
Regarding Applicant's argument that the prior art fails to teach a knowledge base "trained with data comprising image segments having previously classified damage levels," this argument ignores the express disclosures in the cited secondary reference. While Mahadevappa teaches the underlying framework of utilizing threshold defect classes mapped to remaining life, Kuwertz is relied upon to teach the specific machine-learning architecture. Kuwertz explicitly teaches providing decoded images to a neural network to determine degradation characteristics representing modes of degradation (Kuwertz, [Abstract]). Kuwertz further explicitly discloses that the deep convolutional neural network is trained to predict characteristics based on a "training corpus of images with established conclusions" evaluated by experts. Kuwertz provides specific, categorized examples of these previously classified damage levels, such as "worn" (WT), "chipped" (CT), "broken" (BT), and "spalled" (SP) [FIGS. 4A-4G]. Therefore, Kuwertz explicitly teaches a knowledge base trained with data comprising image segments having previously classified damage levels.
Regarding Applicant's argument that combining Mahadevappa and Kuwertz would change the principle of operation because Kuwertz evaluates oilfield tools and uses different physical sensors, this argument is not persuasive. This argument attacks the references individually and mischaracterizes the nature of the combination. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference. In re Keller, 642 F.2d 413, 425 (CCPA 1981). The rejection does not propose physically replacing Mahadevappa's optical fiber hoist sensors with an oilfield tool scanner. Rather, the rejection relies on Kuwertz for teaching the algorithmic implementation of image capture, neural-network evaluation, and reporting logic on a mobile device architecture. Applying Kuwertz's known mobile-device software capabilities and machine-learning training techniques to Mahadevappa's rope-inspection workflow is a predictable use of prior art elements according to their established functions. This combination yields the predictable result of improved field portability and machine-learning accuracy, without changing Mahadevappa's underlying principle of analyzing acquired images to detect rope defects.
The combination would have produced predictable results with a reasonable expectation of success because the references use known computer-implemented image analysis and machine-learning techniques for their established purposes: Mahadevappa’s rope defect detection mapped to remaining life, and Kuwertz’s neural network trained on pre-classified damage data operating on a mobile platform. Therefore, the rejection under 35 U.S.C. §103 is maintained.
Based on these facts, this action is made FINAL.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1–22 are rejected under 35 U.S.C. §103 as being unpatentable over Mahadevappa (Mahadevappa et al, US 2020/0118259 A1, 2020), in view of Plaia (Plaia et al., US 2021/0190756 A1, 2021; provided by Applicant's disclosure in [0021]), further in view of Kuwertz (Kuwertz et al., US 2019/0279356 A1, 2019).
Regarding claim 1, with deficiencies of Mahadevappa noted in square brackets [ ], Mahadevappa teaches a computer-implemented method for inspecting a rope [ having a plurality of fibers ], comprising the steps of:
Using an image capture device [ using a mobile device ], capturing visual data of said rope, wherein said visual data includes one or more sections of said rope along a length thereof;
( Mahadevappa, in [0035-0036] & [0044-0045], teaches an image capture device monitors the rope (flexible members) and generate image data , “including the captured image” and “the location of the rope segment”; “indicates a location of the length of rope” [corresponds to section along length of the rope] )
analyzing said visual data using a knowledge base implemented within logic of a control system [ of said mobile device, wherein said knowledge base is trained with data comprising image segments having previously classified damage levels. ]
( Mahadevappa, in [0037] & [0046], teaches receiving and using reference image data and threshold setting information (defect types / classes; application / rope-type dependent), and compares current image data to reference image data to determine defects based on thresholds [corresponds to analyzing the visual data using a knowledge base] )
calculating, from said knowledge base, an expected life for said rope;
( Mahadevappa, in [0037], teaches threshold setting information includes defect classes mapped to remaining life or strength of the rope [corresponds to calculating an expected life for the rope from the knowledge base]; and Mahadevappa further teaches using machine learning and field service data [knowledge base] to correlate [a statistical calculation] defects and optimize remaining life and recommendations, [0047] )
generating a report on said display device [ said mobile device ] displaying said expected life of said rope as calculated from said knowledge base.
( Mahadevappa, [0038–0039] teaches transmitting results to a display and presenting analysis results including rope health, severity of any defects and a recommendation as to when the rope should be repaired / replaced, corresponds to generating a report displaying the life-related outcome. )
As noted above in square brackets, Mahadevappa fails to disclose but Plaia teaches:
a rope having a plurality of fibers
( Plaia [0035]: “the example rope under test comprises a plurality of rope sub-components … each comprising a plurality of rope fibers”, and further explains the rope fibers are grouped into yarns, yarns into strands, and strands combined to form the rope. )
It would have been prima facie obvious to a POSITA, before the effective filing date of the claimed invention, motivated to apply Mahadevappa’s rope inspection and remaining-life determination to a rope having a plurality of fibers, because Mahadevappa is directed broadly to inspection of a flexible member such as a rope and uses defect classification/threshold information that is tailored to the rope type and application, such that the same defect-detection and life-mapping approach predictably applies to different rope constructions. Plaia merely confirms that a “fiber rope” is a rope construction comprising a plurality of rope fibers (grouped into yarns and strands) and that fiber-rope construction types are routinely evaluated for expected/remaining life, thereby reinforcing that Mahadevappa’s rope-based approach encompasses fiber-rope embodiments and would have yielded predictable results with a reasonable expectation of success.
Mahadevappa [as modified by Plaia] still fails to disclose where Kuwertz teaches:
using a mobile device, capturing visual data
( [0021-0024]: Kuwertz teaches a field operator takes a picture or set of pictures or video using an image capturing device such as a smartphone, and the device captures an image or video include multiple images along object of interest. )
analyzing said visual data using a knowledge base implemented within logic of a control system of said mobile device, wherein said knowledge base is trained with data comprising image segments having previously classified damage levels.
( [0020], [0025-0028], [0032]: Kuwertz teaches the mobile device 102 may include two or more connected devices including one that “stores, processes, and/or transmits the images” and the image may be captured in response to guidance received from the processor or a program that is local to the device 102, which corresponds to processing logic implemented on the mobile device in the image capture and evaluation workflow; wherein said knowledge base is a neural network trained with data comprising image segments having previously classified damage levels that "the training corpus of the neural network may be established a priori by one or more trained experts"; at that the neural network develops an algorithm "based on a training corpus of images with established conclusions" regarding degradation characteristic and consumption values; and at that the neural network constructs its predictive model by observing the experts characterization of the objects in the images of the training corpus. )
on said mobile device
( [0045]: Kuwertz teaches transmitting image data “for display thereon” to the device 102, which is a mobile device )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mahadevappa’s rope-inspection system to use Kuwertz’s mobile-device capture, on-device processing logic, trained neural-network knowledge base, and on-device display / reporting, because Kuwertz teaches that a smartphone / tablet can capture still images or video in the field and can include local program logic for capture / processing guidance and for presenting results on the device, which would have predictably improved portability and field usability of Mahadevappa’s rope inspection (allowing an inspector to conveniently capture multiple rope sections along the rope length and receive displayed inspection outputs at the point of inspection), while using known, art-recognized mobile inspection techniques and without changing Mahadevappa’s underlying defect-detection and remaining-life determination principles.
Regarding claim 2, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 1, further comprising the step of processing said visual data to ready said visual data for analysis.
( Mahadevappa, in [0036], teaches image processing to “proper the image” for defect detection. Kuwertz, in [0042], also teaches preprocessing and filtering images. )
Regarding claim 3, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 2, wherein the step of processing said visual data further comprises the step of tiling said visual data, wherein said visual data is broken into multiple image segments along said section of said rope.
( [0045]: Mahadevappa teaches that each frame monitors a certain length of the rope and splits the frame into smaller frames/windows for analysis against the reference frame. )
Regarding claim 4, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 3, wherein for the step of tiling said visual data, said visual data is cropped to form each said image segment with minimal background.
( [0036]: Mahadevappa teaches cropping image data to a region of interest, eliminate background to prepare the image for further processing and defect detection. )
Regarding claim 5, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 3, wherein for the step of tiling said visual data, contrast of each said image segment is enhanced.
( [0036]: Mahadevappa teaches enhancing images: sharpness, deblur, distortion removal.)
Regarding claim 6, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 3, further comprising generating and assigning a damage level scale to each said image segment along said length.
( [0032], [0039-0040]: Mahadevappa teaches classifying defect types and ranking severity, including defect classes used for operational clearance and health assessment; [0045]: Mahadevappa teaches that each frame monitors a certain length of the rope, which ties frames/segments to rope length [corresponds to segment along said length]. )
Regarding claim 7, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 6, further comprising the step of averaging each said damage level scale.
( Kuwertz, in [0051], expressly teaches calculating an “average degradation value” based on a plurality of consumption amount values. )
Regarding claim 8, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 2, further comprising the step of converting said visual data to grayscale.
( [0044]: Mahadevappa teaches converting image data to black and white for further processing, which is a grayscale conversion. )
Regarding claim 9, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 1, further comprising allowing said knowledge base to be continuously supplemented from a data pipeline.
( in [0033], Mahadevappa teaches updating the system based on collected field failure data to improve detection efficiency. Kuwertz, in [0030], also teaches storing degradation metrics in a database to establish patterns. )
Regarding claim 10, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 1, further comprising allowing said knowledge base to continuously learn to enhance calculations for said expected life.
( Mahadevappa, in [0040], teaches machine learning to optimize defect identification and correlation to remaining life based on field service data. )
Regarding claim 11, Mahadevappa [as modified by Plaia and Kuwertz] teaches the computer-implemented method of claim 1, wherein for the step of capturing said visual data, said mobile device is moved along said rope while said rope remains stationary.
( Kuwertz, in [0032] & [0046], teaches positioning and moving the mobile video-capture device (including by a robotic arm) using direction, angle, distance, elevation guidance, i.e., moving the mobile capture device relative to the object being imaged. )
Regarding claims 12–22, the rationale provided for claims 1–11 is incorporated herein. In addition, the computer-readable storage medium of claims 12–22 corresponds to the method of claims 1–11, and performs the steps disclosed herein. Therefore, the claims are all rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEN KUDO whose telephone number is (571)272-4498. The examiner can normally be reached M-F 8am - 5pm.
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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