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 .
Election/Restrictions
Applicant’s election of Invention I, directed to claims 1-2, 4-7, and 12-14, in the reply filed on January 27, 2026, is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)). The cancellation of claims 3, 8-11, 15-16, 18-19, 24-27, 28, 32, 34, 37, 38; and withdrawal of claims 17, 20-23, 28-29, 30, 31, 33, 35-36, is acknowledged.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Regarding claim 4, the limitation “data characterizing one or more time-series infrared images of the industrial asset” lacks antecedent basis as the limitation is already presented in claim 1. Correction is required.
Claim Rejections - 35 USC § 103
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, 4-7, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. 2012/0330569 (“Singh”) in view of U.S. Patent Pub. 2023/0152792 (“Mears”).
Claim 1
Singh discloses a system comprising: an infrared camera configured to acquire one or more time-series infrared images of an industrial asset (paragraph [0050], infrared camera 260 for time series acquisition); a computing device including at least one data processor (image processor 175), and a memory coupled to the at least one data processor and storing instructions (memory 180), which when executed, cause the at least one data processor to perform operations comprising: receiving data characterizing the one or more time-series infrared images of the industrial asset (paragraphs [0048-0050], data received), determining an area of interest of the industrial asset within the one or more time- series infrared images (paragraph [0048], area of interest), determining a plurality of defects associated with pixels within the area of interest (paragraph [0048], defects detected), wherein each defect of the plurality of defects is determined based on pixel-wise assignment of at least one defect category selected from a plurality of defect categories for each pixel of the one or more time-series infrared images and each defect is represented by a cluster of pixels in which each pixel is assigned an identical defect category (paragraph [0007]), and wherein each defect category is associated with a lifecycle of corrosion under insulation of the industrial asset, and providing the determined plurality of defects within the area of interest in the one or more time-series infrared images of the industrial asset (paragraph [0029, 0048-0050]).
Singh does not appear to explicitly disclose determining defects by machine learning algorithm.
Mears discloses defect detection by machine learning (paragraph [0018, 0105]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated a machine learning algorithm, as disclosed by Mears, into the device of Singh, for the purpose of tracking progressive damage of a defect (Mears, paragraph [0049]).
Claim 2
Singh in view of Mears discloses the system of claim 1, wherein the plurality of defect categories includes a defect-free category, an insulation damage category, a moisture accumulation category, a metal corrosion category, and a deep-metal loss corrosion category (paragraph [0029], voids, cracks, corrosion, delamination), and further wherein the lifecycle of corrosion under insulation associated with each defect category includes a sequence of progressive stages of corrosion of the industrial asset (Mears, paragraph [0049]).
Claim 4
Singh in view of Mears discloses the system of claim 1, wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising: receiving data characterizing one or more time-series infrared images of the industrial asset acquired via an infrared camera; annotating the one or more time-series infrared images with ground-truth annotations based on physical examination of the industrial asset; and training the machine learning algorithm based on the annotated one or more time-series infrared images (Mears, paragraph [0102-0105], updating; Singh, paragraph [0050]).
Claim 5
Singh in view of Mears discloses the system of claim 1, wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising: receiving data characterizing one or more training configuration parameters associated with at least one defect category selected from the plurality of defect categories and associated with the lifecycle of corrosion under insulation of the industrial asset; generating a plurality of defect image patches based on the data characterizing one or more training configuration parameters, the plurality of defect image patches including the defect; overlaying one or more of the defect image patches onto defect-free time-series image data of the industrial asset, the defect-free time-series image data devoid of any defects of the industrial asset; generating time-series image training data based on the overlaying, wherein the generated time-series image training data comprises ground-truth annotations corresponding to one or more defect categories, the ground-truth annotations determined based on the one or more training configuration parameters; and training the machine learning algorithm using the generated time-series image training data (Mears, paragraph [0102-0105], updating; Singh, paragraph [0050]).
Claim 6
Singh in view of Mears discloses the system of claim 1, wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising: receiving field-originated time-series infrared images of the industrial asset acquired via an infrared camera and annotated with ground-truth annotations based on physical examination of the industrial asset; receiving time-series image training data generated based on overlaying one or more defect image patches onto defect-free time-series image data of the industrial asset, the defect-free time- series image data devoid of any defects of the industrial asset, wherein the time-series image training data comprises ground-truth annotations corresponding to one or more defect categories; and training the machine learning algorithm based on a combined training dataset including the field-originated time-series infrared images and the generated time-series image training data (Mears, paragraph [0102-0105], updating; Singh, paragraph [0050]).
Claim 7
Singh in view of Mears discloses the system of claim 5, wherein the data characterizing one or more training configuration parameters further include a surface temperature associated with the industrial asset, a temperature of a fluid within the industrial asset, a type of defect, a size of a defect, a shape of a defect, a depth of a defect, a location of a defect, a metal thickness of the industrial asset, a metal type of the industrial asset, or a thickness of the insulation (Singh, paragraph [0058], temperature increase by defect).
Claim 12
Singh in view of Mears discloses the system of claim 5, wherein the data characterizing one or more training configuration parameters includes a defect depth or a defect size, and generating the plurality of defect image patches further comprises: determining, using a first physical model of temperature propagation through a cross- section of the industrial asset, at least one temperature profile of the industrial asset; generating, based on the defect depth or the defect size and the determining a surface temperature for each pixel included in the plurality of detect image patches; and providing the surface temperature for each pixel in the plurality of defect image patches, wherein the surface temperature is provided in the plurality of defect image patches as a cross- sectional view of the industrial asset (Mears, paragraph [0068], defect size).
Claim 13
Singh in view of Mears discloses the system of claim 5, wherein the data characterizing one or more training configuration parameters includes a defect location corresponding to a corrosion origination point, and generating the plurality of defect image patches further comprises: determining, using a second physical model of temperature propagation across a surface of the industrial asset, at least one surface temperature profile of the industrial asset; generating, based on the defect location and the determining, a surface temperature distribution within the plurality of defect image patches; and providing the surface temperature distribution in the plurality of defect image patches, wherein the surface temperature distribution extends across the surface of the industrial asset from the defect location corresponding to the corrosion origination point toward edges of the plurality of defect image patches (Mears, paragraph [0068], defect misalignment).
Claim 14
Singh in view of Mears discloses the system of claim 13, wherein generating the plurality of defect image patches further comprises applying a camera noise model corresponding to the infrared camera to the plurality of defect image patches (Singh, paragraph [0007], background filter).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERICA S Y LIN whose telephone number is (571)270-7911. The examiner can normally be reached M-F 8-4, TW M,W.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Douglas X Rodriguez can be reached at (571) 431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERICA S LIN/Primary Examiner, Art Unit 2853