DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/06/2026 has been entered.
Claims 1-20 are pending. Claims 1 and 11 are amended.
Claim Rejections - 35 USC § 101
Previous rejection under 35 USC 101, is withdrawn in view of Applicant's reply filed 08/06/2026.
To note: Claims 1-20 are patent eligible with regards to the Patent Subject Matter Eligibility Guidance. The claims, taken as a whole amount to a practical application of the judicial exception see MPEP 2106.04(d) (a claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kherroubi et al. [US Patent Number 10,121,261 B2] in view of He et al. [US 2018/0120477 A1] and Tian et al. [US 11,860,326 A1].
Regarding claim 1, Kherroubi teaches a method comprising:
disposing an azimuthal borehole measurement tool into a borehole (The well-logging system 10 may be conveyed through a geological formation 14 via a wellbore 16. A downhole tool 12 may be conveyed on a cable 18 via a logging winch system 20 - C4L1-5);
obtaining an azimuthal borehole image with the azimuthal borehole measurement tool (block 72, the data processing system 28 may receive borehole image data that may have been acquired by the downhole tool 12 described above or by some other logging tool. The borehole image data may be provided in a similar fashion as the flat image - C5L45-64);
running a pre-processing action on the azimuthal borehole image to obtain a smart gradient image (at block 80, the data processing system 28 dequantizes the resulting borehole image. In one embodiment, when dequantizing the borehole image, the data processing system 28 may apply a Gaussian blur (σ) to the borehole image. Generally, the borehole image may include certain quantization or noise artifacts. These artifacts are due to the gradient direction of a quantized image not being uniformly distributed - C6L38-45) (use an image gradient, which can be computed with a finite difference scheme – C6L56-66) (image gradients field – C7L4-8);
running a sinusoidal pattern search action on the smart gradient image to form a raw dip of the azimuthal borehole image (blocks 84, 86, 88, C8L33-64, C9L8-18); and
running a dip post-processing action to form a final dip (block 90, determine if a sinusoid having the predetermined dip orientation is meaningful or actually present at a predetermined measured depth h. From this operation, the data processing system 28 may extract the individual dips from the borehole image – C9L18-31)).
While Kherroubi teaches the above limitations and determining one or more dip features (via a-contrario validation algorithm – C9L32-39), Kherroubi does not specifically disclose one or more dip features comprises symmetry, separation, or dip continuity.
However, He teaches a method and system for dip picking and zonation of highly deviated well images by determining one or more dip features comprising symmetry, separation, or dip continuity (Once the dips have been identified or marked on the borehole image 44, the processor 30 may compute (block 114) a symmetry probability (e.g., vertical symmetry probability) based on the orientation (e.g., inclination and/or azimuth) of the dip- 0053).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the teachings of Kherroubi to further include determining additional dip features as taught by He for the purposes of quality checking the determined dip information to provide more accurate results for enhancing geological interpretations (He - 0005, 0053).
While Kherroubi teaches the above limitations, Kherroubi does not specifically disclose generating multiple gradient images and fusing the multiple gradient images to obtain a smart gradient image.
However, Tian teaches an image fusion method of generating multiple gradient images and fusing the multiple gradient images to obtain a smart gradient image (To combine attribute images and amplitude gradient images, image fusion techniques such as wavelet transform, ellipsoidal dilation, and high-frequency and low-frequency fusion are used to fuse the dip and amplitude gradient attributes into a new attribute called superimposed fault attribute – C18L9-14).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the teachings of Kherroubi to further include generating multiple gradient images and fusing the multiple gradient images as taught by Tian for the benefits of achieving advantages of both: determining the accurate location of the fault, displaying a large amount of detailed information, and compensating for each other's shortcomings (Tian - C18L15-19).
Regarding claim 2, Kherroubi teaches the pre-processing action comprises data quality control performs a quality check on the azimuth borehole image (at block 80, the data processing system 28 dequantizes the resulting borehole image. In one embodiment, when dequantizing the borehole image, the data processing system 28 may apply a Gaussian blur (σ) to the borehole image. Generally, the borehole image may include certain quantization or noise artifacts. These artifacts are due to the gradient direction of a quantized image not being uniformly distributed, but, instead, having a discrete irregular distribution. The artifacts may become visible after local image orientations regarding the borehole image are determined (block 82) using a Hough transform or the like. Examples of the artifacts will be discussed below with reference to FIG. 5. As such, by dequantizing the borehole image, the data processing system 28 may filter at least a portion of the artifacts from the Hough transformed image. The applied Gaussian blur may be a predetermined blur or may be optimized depending on the features shown on the borehole image – C6L38-55).
Regarding claim 3, Kherroubi teaches the pre-processing action comprises quality-based resampling is performed on the azimuthal borehole image with data quality control to form a resampled azimuth borehole image (resample the borehole image at the provided scale parameter - C6L15-20).
Regarding claim 4, Kherroubi teaches taking a difference of data values between two adjacent data points along a depth direction or using a gradient filter (window depth – C6L1-20).
Regarding claim 5, Kherroubi teaches the sinusoidal pattern search action comprises a non- uniform Hough transform to search for dips in the smart gradient image (Hough transform - C6L48-52).
Regarding claim 6, Kherroubi teaches the non-uniform Hough transform is performed in a three- dimensional domain to search for sinusoids with different depths, amplitudes, and phases (Based on the sinusoid 58, the data processing system 28 may then determine the dip orientation (dip inclination and azimuth), and, after that, the measured depth of the dip using the sinusoid characteristics (e.g. measured depth, amplitude, phase – C5L24-30).
Regarding claim 7, Kherroubi teaches the Hough transforms implements median based voting, wherein median based voting aggregates the median value of values comprising gradients along a sinusoid and forms a 3D Hough volume (Hough voting - C8L33-64).
Regarding claim 8, Kherroubi teaches recording amplitude and phase of a maximum voting for each depth of the 3D Hough volume (maximum in the hough space - C8L33-64).
Regarding claim 9, Kherroubi teaches the dip post-processing action comprises determining one or more dip features, wherein one or more dip features comprises symmetry, separation, or dip continuity (via a-contrario validation algorithm – C9L32-39).
Regarding claim 10, Kherroubi teaches computing a score for the raw dip based at least in part on the one or more dip features (vote score - C8L41-45).
Regarding claim 11, Kherroubi teaches a system comprising:
an azimuthal borehole measurement tool disposed in a borehole (The well-logging system 10 may be conveyed through a geological formation 14 via a wellbore 16. A downhole tool 12 may be conveyed on a cable 18 via a logging winch system 20 - C4L1-5) configured to obtain an azimuthal borehole image (block 72, the data processing system 28 may receive borehole image data that may have been acquired by the downhole tool 12 described above or by some other logging tool. The borehole image data may be provided in a similar fashion as the flat image - C5L45-64); and an information handling system configured to (data processing system – C4L37-42):
run a pre-processing action on the azimuthal borehole image to obtain a smart gradient image (at block 80, the data processing system 28 dequantizes the resulting borehole image. In one embodiment, when dequantizing the borehole image, the data processing system 28 may apply a Gaussian blur (σ) to the borehole image. Generally, the borehole image may include certain quantization or noise artifacts. These artifacts are due to the gradient direction of a quantized image not being uniformly distributed - C6L38-45) (use an image gradient, which can be computed with a finite difference scheme – C6L56-66) (image gradients field – C7L4-8);
run a sinusoidal pattern search action on the smart gradient image to form a raw dip of the azimuthal borehole image (blocks 84, 86, 88, figure 3, C8L33-64, C9L8-18); and
run a dip post-processing action to form a final dip (block 90, determine if a sinusoid having the predetermined dip orientation is meaningful or actually present at a predetermined measured depth h. From this operation, the data processing system 28 may extract the individual dips from the borehole image – C9L18-31)).
While Kherroubi teaches the above limitations and determining one or more dip features (via a-contrario validation algorithm – C9L32-39), Kherroubi does not specifically disclose one or more dip features comprises symmetry, separation, or dip continuity.
However, He teaches a method and system for dip picking and zonation of highly deviated well images by determining one or more dip features comprising symmetry, separation, or dip continuity (Once the dips have been identified or marked on the borehole image 44, the processor 30 may compute (block 114) a symmetry probability (e.g., vertical symmetry probability) based on the orientation (e.g., inclination and/or azimuth) of the dip- 0053).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the teachings of Kherroubi to further include determining additional dip features as taught by He for the purposes of quality checking the determined dip information to provide more accurate results for enhancing geological interpretations (He - 0005, 0053).
While Kherroubi teaches the above limitations, Kherroubi does not specifically disclose generating multiple gradient images and fusing the multiple gradient images to obtain a smart gradient image.
However, Tian teaches an image fusion method of generating multiple gradient images and fusing the multiple gradient images to obtain a smart gradient image (To combine attribute images and amplitude gradient images, image fusion techniques such as wavelet transform, ellipsoidal dilation, and high-frequency and low-frequency fusion are used to fuse the dip and amplitude gradient attributes into a new attribute called superimposed fault attribute – C18L9-14).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the teachings of Kherroubi to further include generating multiple gradient images and fusing the multiple gradient images as taught by Tian for the benefits of achieving advantages of both: determining the accurate location of the fault, displaying a large amount of detailed information, and compensating for each other's shortcomings (Tian - C18L15-19).
Regarding claim 12, Kherroubi teaches the pre-processing action comprises data quality control performs a quality check on the azimuth borehole image (at block 80, the data processing system 28 dequantizes the resulting borehole image. In one embodiment, when dequantizing the borehole image, the data processing system 28 may apply a Gaussian blur (σ) to the borehole image. Generally, the borehole image may include certain quantization or noise artifacts. These artifacts are due to the gradient direction of a quantized image not being uniformly distributed, but, instead, having a discrete irregular distribution. The artifacts may become visible after local image orientations regarding the borehole image are determined (block 82) using a Hough transform or the like. Examples of the artifacts will be discussed below with reference to FIG. 5. As such, by dequantizing the borehole image, the data processing system 28 may filter at least a portion of the artifacts from the Hough transformed image. The applied Gaussian blur may be a predetermined blur or may be optimized depending on the features shown on the borehole image – C6L38-55).
Regarding claim 13, Kherroubi teaches the pre-processing action comprises quality-based resampling is performed on the azimuthal borehole image with data quality control to form are sampled azimuth borehole image (resample the borehole image at the provided scale parameter - C6L15-20).
Regarding claim 14, Kherroubi teaches the information handling system is further configured to take a difference of data values between two adjacent data points along a depth direction or using a gradient filter (window depth – C6L1-20).
Regarding claim 15, Kherroubi teaches the sinusoidal pattern search action comprises a non-uniform Hough transform to search for dips in the smart gradient image (Hough transform - C6L48-52).
Regarding claim 16, Kherroubi teaches the non-uniform Hough transform is performed in a three- dimensional domain to search for sinusoids with different depths, amplitudes, and phases (Based on the sinusoid 58, the data processing system 28 may then determine the dip orientation (dip inclination and azimuth), and, after that, the measured depth of the dip using the sinusoid characteristics (e.g. measured depth, amplitude, phase – C5L24-30).
Regarding claim 17, Kherroubi teaches the Hough transforms implements median based voting, wherein median based voting aggregates the median value of values along a sinusoid and forms a 3D Hough volume (Hough voting - C8L33-64).
Regarding claim 18, Kherroubi teaches the information handling system is further configured to record amplitude and phase of a maximum voting for each depth of the 3D Hough volume (maximum in the hough space - C8L33-64).
Regarding claim 19, Kherroubi teaches the dip post-processing action comprises determining one or more dip features, wherein one or more dip features comprises symmetry, separation, or dip continuity (via a-contrario validation algorithm – C9L32-39).
Regarding claim 20, Kherroubi teaches the information handling system is further configured to compute a score for the raw dip based at least in part on the one or more dip features (vote score - C8L41-45).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PENG et al. (US Patent Application Publication 2022/0148143 A1) discloses an image fusion method based on gradient domain mapping.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICKY GO whose telephone number is (571)270-3340. The examiner can normally be reached on Monday through Friday from 9:00 a.m. to 5:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICKY GO/Primary Examiner, Art Unit 2857
Prosecution Insights