AUTOMATED MONITORING OF DEFECTS IN WIRELINE CABLES

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 . DETAILED ACTION Information Disclosure Statement The information disclosure statement filed on 02/09/2026 has been entered and considered by the examiner. Drawings The drawings filed on 05/02/2025, has been accepted for examination. Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Harjuhahto et al. (2021/0041375 Aa1). Regarding claim 1, Harjuhahto discloses a method for automated detection of defects in wireline cables is a method for surface scanner for surface defect detection of a cable (figs. 1-4) (see abstract), comprising: an analysis part 4 analyzing, using a trained image analysis model a neural network trained, one or more image frames captured by plurality of measurement sensors 6/image capture device of a wireline cable 2 portion at a given time instance using an imaging subsystem a measuring part 3, wherein the model is trained to predict the presence of defects in imaged wireline cables (see abstract)[pars. 0003, 0016-17]; - based on an output/measurement data of the trained image analysis model neural network trained, the measurement data providing defect detection data determining the presence of one or more defects in the imaged wireline cable portion; and- the neural network is trained to output the presence or absence a defect detect if one or more defects are detected, generating an output [pars. 0016-30]. As to claims 2-4 , Harjuhahto also discloses a structure (figs. 1-4) that is use in a method/system that is implementing limitations such as, comprising, initially, operating the imaging subsystem measuring part 3 to capture by the plurality of measurement sensors 6/image capture device the one or more image frames measurement data, wherein the imaging subsystem comprises plurality of measurement sensors 6/image capture device four or more sensors 6 includes at least three imaging sensors , each operable to capture a corresponding image frame of the wireline cable 2 portion, as can be seen depicted in drawing (fig. 2) (claim 2); wherein the imaging sensors measurement sensors 6/image capture device are arranged around an imaging area to provide a 3600 circumferential view of the wireline cable 2 portion, and are supported by an imaging hardware assembly, as can be seen depicted in drawing (fig. 2) (claim 3); and wherein the imaging sensors measurement sensors 6/image capture device are arranged in a triangular configuration [par. 0022] as can be seen depicted in drawing (fig. 2) (claim 4). As to claims 5-9, Harjuhahto further discloses a structure (figs. 1-4) that is use in a method/system that is implementing limitations such as, wherein the trained image analysis model neural network trained (see abstract) [pars. 0005, 0008, 0027-28] is a trained YOLOX model (claim 5); wherein prior to inputting the image frames included in the measurement data, a receiver 9 for the measurement data combining the image frames to generate a stacked image frame, and inputting the stacked image frame into the trained image analysis model included in the and a processor 10 for processing the measurement data providing defect detection data [pars. 0016-30] (claim 6); wherein the trained image analysis model neural network trained is also trained to predict one or more defect features (types of defects) (see abstract) [pars. 0005, 0008, 0027-28, 0036] (claim 7); wherein the defect features include one or more of defect type, defect severity, pixel location of defect within the one or more image frames that is included in the measurement data [pars. 0036] (claim 8); and wherein the output the measurement data comprises a user notification of a detected defect [par. 0036] (claim 9). For the purposes of clarity, Applicant discloses that it is known in the art that neural network is trained YOLOX model (applicant spec. [par. 0153]). As to claims 10-13, Harjuhahto also discloses a structure (figs. 1-4) that is use in a method/system that is implementing limitations such as, the processor 10/process supervision unit 20 generating a defect output dataset comprising one or more of: (i) an indication of the presence of the defect (i.e. in the image frames the measurement data; and (ii) one or more defect features; and - associating the defect output dataset with one or more of the image frames the measurement data pars. 0016-36] (claim 10); iterating the method for a length of wireline and generating a wireline output dataset comprising a plurality of defect output datasets is repeating the defect detection process, action, or set of instructions again and again, over time during the manufacturing process [pars. [pars. 0036-42] (claim 11); wherein the one or more defects comprise one or more first defects, and the method further comprising: using laser method to acquired measurement data [pars. 0005, 0022] anticipates and implied analyzing laser measurement data, of the wireline cable portion captured using a laser subsystem, using a trained laser analysis model neural network trained, the trained laser analysis model being trained to predict the presence of one or more second defects in the wireline cable portion based on the laser measurement data (types of defects) (see abstract) [pars. 0005, 0008, 0027-28, 0036]; - based on the analysis by the neural network trained, the measurement data providing defect detection data determining the presence of one or more second defects in the wireline cable portion; and the neural network is trained to output the presence or absence a defect detect if one or more second defects are detected, generating the defect output dataset to include an indication of the one or more second defects [pars. 0016-30] (claim 12), and further comprising operating the laser subsystem measuring part 3 to capture with the plurality of measurement sensors 6/image capture device the laser measurement data measurement data of the wireline cable 2 portion, wherein the laser subsystem measuring part 3 comprises one or more laser sources is anticipated in the method of using laser method to acquired measurement data [pars. 0005, 0022], as well as laser sensors which generate the laser measurement data (claim 13). As to claim 14, Harjuhahto also discloses Neural networks are capable of learning complicated nonlinear (machine learning algorithm XGBoost) relationships from sets of training examples. In supervised training both the inputs and outputs are provided. In unsupervised training, the network is provided with inputs but not with desired outputs. In unsupervised training, the neural network learns from the measurement data acquired during a run of a cable 2 production process [par. 0028] anticipates wherein the trained laser analysis model is a binary classification gradient-boosted tree with XGBoost. As to claims 15 and 19, Harjuhahto discloses a system for automated detection of defects in wireline cables is a method for surface scanner for surface defect detection of a cable (figs. 1-4) (see abstract), comprising: - an imaging subsystem measuring part 3 configured to generate one or more image frames measurement data, captured by the plurality of measurement sensors 6/image capture device of a wireline cable (2) portion at a given time instance as can be seen depicted in drawing (fig. 2) (see abstract) [pars. 0003, 0016-17]; - a cloud storage 12 a memory for storing a trained image analysis model neural network trained, wherein the model is trained to predict the presence of defects in imaged wireline cables [par. 0025] (Harjhahto, claim 3); and - at least one processor a processor 10 for processing the measurement data providing defect detection data [pars. 0016-30] coupled to the memory and imaging subsystem, and configured for: - analyzing the one or more image frames using the trained image analysis model neural network trained; - based on an output/measurement data of the trained image analysis model neural network trained, the measurement data providing defect detection data determining the presence of one or more defects in the imaged wireline cable portion; and- the neural network is trained to output the presence or absence a defect detect if one or more defects are detected, generating an output (types of defects) (see abstract) [pars. 0005, 0008, 0027-28, 0036]. For the purposes of clarity, the structure recited in claim 19 is symmetrical to the structure recited in claim 15, as such, the system claim 19 is taught/suggested by the functions shown/stated/set forth with regards to claim 15 as rejected above as being anticipated by Harjuhahto. As to claims 16-17 , Harjuhahto also discloses a structure (figs. 1-4) that is use in a method/system that is implementing limitations such as, comprising, initially, operating the imaging subsystem measuring part 3 to capture by the plurality of measurement sensors 6/image capture device the one or more image frames measurement data, wherein the imaging subsystem comprises plurality of measurement sensors 6/image capture device four or more sensors 6 includes at least three imaging sensors , each operable to capture a corresponding image frame of the wireline cable 2 portion, as can be seen depicted in drawing (fig. 2) (claim 16); and wherein the imaging sensors measurement sensors 6/image capture device are arranged around an imaging area to provide a 3600 circumferential view of the wireline cable 2 portion, and are supported by an imaging hardware assembly, as can be seen depicted in drawing (fig. 2) (claim 17); wherein the trained image analysis model neural network trained is also trained to predict one or more defect features (types of defects) (see abstract) [pars. 0005, 0008, 0027-28, 0036] (claim 18); As to claim 20, Harjuhahto also discloses wherein the laser subsystem measuring part 3 comprises one or more laser sources is anticipated in the method of using laser method to acquired measurement data [pars. 0005, 0022], as well as laser sensors which generate the laser measurement data. Additional Prior Art The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references listed in the attached form PTO-892 teach of other prior art method/system for automated detection of defects in wireline cables. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Isiaka Akanbi whose telephone number is (571) 272-8658. The examiner can normally be reached on 8:00 a.m. - 4:30 p.m. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tarifur R. Chowdhury can be reached on (571) 272-2287. The fax phone number for the organization where this application or proceeding is assigned is 703-872-9306. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /ISIAKA O AKANBI/Primary Examiner, Art Unit 2877