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 (IDS) submitted on 4/7/2025 is 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 § 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 (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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 10-12, 14, 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu et. al. (United States Patent Application Publication US 2022/0335253 A1).
Regarding claim 1 and claim 10, Xu discloses a system and method, comprising: a computing system comprising one or more processors, memory, and instructions stored on the memory and executable by the one or more processors to perform operations comprising: receiving borehole image data (Xu et. al. Abstract, Fig. 2 and 3); segmenting the borehole image data into a plurality of patches (Xu et. al. [0008]: The deep learning model may include a UNet classifier. The UNet classifier may include: a first stage configured to perform a pixel-level classification and classify each pixel or each patch of pixels into one of a set of integrity labels, and a second stage of using morphological patterns to discriminate defect patterns according to a respective size of each defect pattern), wherein each patch of the plurality of patches is representative of a fixed size segment of the borehole image data (Xu et. al. [0030]: Further referring to panel 209, which shows a profile of a tool and borehole configuration); determining one or more temporal dependencies between each patch and one or more surrounding patches of the plurality of patches (Xu et. al. [0033]: Implementations may apply a multiclass segmentation technique based on U-Net, which is a specialized convolutional neural network, to inspection log images. The first stage may be followed by a second stage where the morphological patterns are used to discriminate small versus large defect patterns that are semantically meaningful to operation. The corrosion logs inspected over time (i.e., temporal dependencies) identify borehole defect patterns and trends over time, enabling proactive maintenance and risk mitigation.); and generating, via a defect prediction model, defect identification image data representative of a continuous indication of a defect in a structure of a borehole based on the plurality of patches and the one or more temporal dependencies (Xu et. al. [0036]-[0037], Figures 4 and 5: representative of defect prediction model in 411, “Large area integrity prediction”: In various implementations, the labeling database can be a training ground for the deep learning models of the present disclosure. In other words, the implementations can utilize deep learning to develop image classification based on labeling provided by human expert on existing inspection log data. The labeling database can expand as more labeling data is available. Human expertise can be leveraged and then extrapolated to image data and new data.).
Regarding claim 18, which is one or more tangible non-transitory computer-readable memory media, comprising: processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of claim 10, which the rejection analysis is incorporated herein.
Regarding claim 2 and claim 11, Xu et. al. discloses the system of claim 1 and method of claim 10, wherein the plurality of patches form a two-dimensional (2D) mapping of the borehole image data (Xu et. al. [0008], [0012], [0036]: The plurality of inspection logs may include logs from at least one of: a multi-finger cased-hole caliper tool, a flux-leakage tool, an electromagnetic (EM) phase shift tool, and an ultrasonic imaging tool. The plurality of inspection logs may include at least one of a low-frequency thickness image and at least one of a high-frequency discrimination image, the low-frequency thickness image and the high-frequency discrimination image may be based on, at least in part, the EM phase shift tool.).
Regarding claim 19, which is the one or more tangible non-transitory computer-readable memory media of claim 18, wherein the instructions that, when executed by the one or more processors, are configured to carry out the method of claim 11, which the rejection analysis is incorporated herein.
Regarding claim 3 and claim 12, Xu et. al. discloses the system of claim 1 and method of claim 10, comprising: determining a severity of the defect based on the defect identification image data, wherein the severity of the defect is associated one or more safety factors associated with the defect, wherein the one or more safety factors comprise a length of defect, a depth of defect, corrosion level associated with the defect, or any combination thereof (Xu et. al. [0024]-[0026]: The implementations can automate the interpretation of casing/tubing inspection logs such as the corrosion logs by integrating all the available log measurement information, such as multi-finger caliper logs, the casing and/or tubing summary report, low-frequency pipe thickness image, and high-frequency discrimination image); generating a status representative of the severity of the defect (Xu et. al. [0033] The first stage may be followed by a second stage where the morphological patterns are used to discriminate small versus large defect patterns that are semantically meaningful to operation.); and initiating one or more actions to address the defect in the borehole of a hydrocarbon system wherein the one or more actions comprise an adjustment to equipment of the hydrocarbon system, a shutdown action, a maintenance action, a borehole inspection action, or any combination thereof (Xu et. al. [0026] Electromagnetic (EM) based wireline corrosion logging tools are more frequently run in cased wells to monitor the integrity of pipes such as casing or tubing. EM corrosion log tools can deliver valuable monitoring data while running in hole. [0037] The labeling database can be a training ground for the deep learning models, and the implementations can utilize deep learning to develop image classification based on labeling provided by human expert on existing inspection log data).
Regarding claim 20, which is the one or more tangible non-transitory computer-readable memory media of claim 18, wherein the instructions that, when executed by the one or more processors, are configured to cause the one or more processors to carry out the method of claim 12, which the rejection analysis is incorporated herein.
Regarding claim 6 and claim 14, Xu et. al. discloses the system of claim 1 and method of claim 10, wherein determining the one or more temporal dependencies each patch and the plurality of patches comprises: identifying one or more characteristics of a specific patch of the plurality of patches (Xu et. al. [0013] The UNet classifier may include: a first stage configured to perform a pixel-level classification and classify each pixel or each patch of pixels into one set of integrity labels, and a second stage of using morphological patterns to discriminate defect patterns according to a respective size of each defect pattern.); determining a temporal position of the specific patch in relation to each additional patch of the plurality of patches, wherein the temporal position of the specific patch is representative of a position of a captured area of the structure of the borehole in the borehole image data; and appending the temporal position to the one or more characteristics (Xu et. al. [0036] The implementations can use patch-based image classifiers such as those based on convolutional neural network (CNN) where each casing and/or tubing inspection image patch are classified into one of the integrity states and then assigned to the center pixel of the patch of pixels, and averaged over adjacent patches. In various examples, the image patches may overlap in space. The integrity state of each pixel can be determined from the patch classification label where this pixel is at the center location.).
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.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et. al. (United States Patent Application Publication US 2022/0335253 A1) in view of Benslimane et. al. (United States Patent US 12,024,993 B2).
Regarding claim 7 and claim 15, Xu et. al. discloses the system of claim 6 and method of claim 14. However, Xu et. al. fails to disclose wherein the one or more characteristics comprise a batch size, a channel size, a height, a width, or any combination thereof.
Benslimane et. al. teaches wherein the one or more characteristics comprise a batch size, a channel size, a height, a width, or any combination thereof (Benslimane et. al. col. 17, lines 64-67: One or more controllers may combine dimensions (e.g., length, width, and/or depth) of the defects with known characteristics (e.g., dimensions, grade, and/or strength) of the tubular structure to estimate its remaining strength). This is important to the claimed invention because the characteristics determine the quality of the borehole being examined. Without knowing these characteristics, it would be difficult to set standards for the structure. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Xu et. al. and Benslimane et. al. so that the characteristics of the borehole are included as part of the inspection solution.
Allowable Subject Matter
Claims 4, 5, 8, 9, 13, 16, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
No prior art has been found to capture all of the essential elements disclosed in the claims, alone or in combination. The features involve detecting defects within patches and applying the defect prediction model via a temporal entropy loss function, a confidence value associated with the continuous defect, discontinuities in detection of the defect, an ability to eliminate noise from the borehole images, and a temporal bridging post-processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Al Ibrahim et. al. (US 20230101218 A1) discloses a method and system for determining a noise-attenuated wellbore image, which includes the optional step of determining a Bayesian uncertainty of the noise-attenuated image. Guner et. al. discloses a method for correcting borehole images for artifact removal using machine learning (US 20230245278 A1).
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 June 11, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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