CTNF 18/406,872 CTNF 90215 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claims 1-20 are pending for examination. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1, 3-4, 6-8, 10-11, 13-15, 17-18 and 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Zhdanov (Pub. No.: US 2013/0018585 A1) . Regarding claim 1, Zhdanov teaches a computer-implemented method that enables detection and mitigation of drilling hazards (abstract, system and method for real time subsurface imaging using EM data from moving platforms) , comprising: determining, using at least one hardware processor (Fig. 7 – Fig. 8, processor 47, para [0050]) , time-domain electromagnetic parameters (Fig. 1 – Fig. 6, abstract, “ At least one desired property, such as conductivity, dielectric permittivity and/or induced polarization parameters, may be derived from the volume image, providing a reconstruction or classification of the physical properties of the geological structures and/or man-made objects. ”. The processor determines the elevation and location of the moving platform (e.g., UAV 21) for collecting the real-time electromagnetic data that represents various properties of the subsurface) ; obtaining, using the at least one hardware processor, data at multiple heights by at least one unmanned aerial vehicle equipped with at least one receiver as a time-domain electromagnetic transmitter emits electromagnetic radiation based on the time-domain electromagnetic parameters (Fig. 1 – Fig. 6, Figs. 11A-11H and para [0045], “ Another embodiment of a real time volume imaging system is illustrated in FIG. 4. A real time volume imaging system 20 is remotely located from a moving platform that may be an unattended aerial system (UAS) 21 that may include EM sources 22 and/or EM sensors 23 attached to the UAS. The UAS may move at some elevation along a survey line L(t) 24 above the surface of an examined medium 25 and the EM data is transmitted in real time from the UAS 21 to the real time volume imaging system 20 (FIG. 4). ”. The UAV comprises of an EM source system and sensor system configured to transmit and to obtain EM data of the medium 6 at various subsurface elevations.) ; and fusing, using the at least one hardware processor, the obtained data to generate a subsurface model (Fig. 9, step 64, and Figs. 11A-11H, para [0050], “ An embodiment of a processor 47 is illustrated in FIG. 8. It will be appreciated that the processor 47 may be implemented in the real time volume imaging systems previously described in FIGS. 1 to 8. The processor 47 may include, for example, a data and code memory 50 for storing EM data received in real time from the data acquisition system 45 via the communications system 46, real time volume imaging computer software and real time volume images, a central processing unit 51 for executing the real time volume imaging computer software on the real time EM data to generate real time volume images, a graphical user interface (GUI) 52 for displaying the real time volume images, and a communications system 53 for real time system interoperability. ”. The processor generates a real time model of the subsurface based on a sequence of EM data.) . Regarding claim 3, Zhdanov teaches the computer implemented method of claim 1, comprising converting the obtained data into images that show changes in components of magnetic fields (Figs. 11A-11H, the model includes images that show changes of the size and the resistivity of the medium at various elevations.) . Regarding claim 4, Zhdanov teaches the computer implemented method of claim 1, comprising processing the obtained data using image-based real-time drilling hazard detection via an image based machine learning model to generate the subsurface model (Fig. 9, and para [0055], “ The aforementioned 3D inversion process is iterated until terminated by at least one operator determined termination condition such as the error decreasing below the preset threshold 59. Once the 3D inversion process is terminated, an interim 3D electrical conductivity model 62 is generated for the given time moment t.sub.n. The interim 3D electrical conductivity model 62 is the real time volume image. If the survey is not complete 63, the aforementioned process is iterated for the next time moment t.sub.n-1 until the survey is completed. If the survey is complete, the interim 3D electrical conductivity model 62 is the final 3D electrical conductivity model 64. ”. The system continuously receives and learns the subsurface based on each iteration of the process.) . Regarding claim 6, Zhdanov teaches the computer implemented method of claim 1, comprising emitting transient electromagnetic pulses into the subsurface without galvanic contact (para [0074], “ Airborne EM systems may include but not be limited to moving sources and/or moving sensors mounted on unattended aerial systems (UAS), fixed-wing aircraft with towed bird systems, fixed-wing aircraft with wing tip systems, fixed-wing aircraft with pod mounted systems, helicopter systems, audio-frequency magnetic (AFMAG) systems, VLF systems, MT systems, and IP systems. ”. The EM data is obtained wireless via EM wave without galvanic contact) . Regarding claim 7, Zhdanov teaches the computer implemented method of claim 1, comprising moving the time-domain electromagnetic transmitter during data capture to enable adaptable data acquisition (Fig. 4, the UAV moves along line 24 to obtain data) . Regarding claim 8, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 1. Therefore, it is rejected for the same reason. Regarding claim 10, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 3. Therefore, it is rejected for the same reason. Regarding claim 11, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 4. Therefore, it is rejected for the same reason. Regarding claim 13, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 6. Therefore, it is rejected for the same reason. Regarding claim 14, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 7. Therefore, it is rejected for the same reason. Regarding claim 15, recites a system that is configured to perform the method of claim 1. Therefore, it is rejected for the same reason. Regarding claim 17, recites a system that is configured to perform the method of claim 3. Therefore, it is rejected for the same reason. Regarding claim 18, recites a system that is configured to perform the method of claim 4. Therefore, it is rejected for the same reason. Regarding claim 20, recites a system that is configured to perform the method of claim 6. Therefore, it is rejected for the same reason . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 2, 5, 9, 12, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhdanov (Pub. No.: US 2013/0018585 A1) in view of Hoang (Pub. No.: US 2021/0018646 A1) . Regarding claim 2, Zhdanov teaches the computer implemented method of claim 1, comprising: updating the subsurface model in real-time using data obtained in real-time (Fig. 9, the system continuously updates the model in a loop from steps 54-63) . Zhdanov fails to teach predicting drilling hazards in real time based on the updated subsurface model. However, in the same field of subsurface detection, Hoang teaches a drilling obstacle detection system that identifies drilling hazards (e.g., karst) based on the subsurface mapping data. See Fig. 7, para [0077], “ Accordingly, as can be appreciated in FIG. 7, in one implementation, a seismic data log 700 is provided along one of the seismic shot lines. A proposed well 702 is projected over the seismic data log 700. The seismic data log 700 illustrates an inverted ground roll data for shear wave velocity, which is generally low in the unconsolidated/reworked rock layer between 10 and 25 feet of depth. Within a few hundred feet from the proposed well 702, there is a velocity anomaly 704 where low velocity (˜3000 ft/s) is surrounded by higher velocity (˜4500 ft/s). The velocity anomaly 704 is identified as a subsurface air-filled karst feature, and flagged as a potential drilling hazard for further evaluation by resistivity data, discussed below with respect to FIGS. 8, 9A, and 9B. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhdanov’s system to further identify potential hazards from the subsurface model to improve drilling safety. Regarding claim 5, Zhdanov teaches the computer implemented method of claim 1, wherein the subsurface model comprises of resistivity data (Figs. 11A-11H) but fails to expressly teach evaluating the subsurface model using physics-based machine learning to confirm at least one drilling hazard in an environment. However, in the same field of subsurface detection, Hoang teaches a drilling obstacle detection system that identifies drilling hazards (e.g., karst) based on the resistivity of the subsurface. See abstract “ A system and method of detecting subsurface karst features includes receiving surface mapping data. A potential surface pad location can be identified in view of the surface mapping data. A resistivity survey for the potential surface pad location can be designed. The resistivity survey can include at least one long line extending through a surface hole for each of one or more wellbores in the potential surface pad location, and a short line extending through the surface hole of one of the one or more wellbores, each short line intersecting the long line substantially at the surface hole of one of the one or more wellbores. High resistivity areas exceeding approximately 150 Ohm per meter can be identified as sub surface karst features within the resistivity survey. ” Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhdanov’s system to further identify potential hazards from the resistivity data of the subsurface to improve safety. Regarding claim 9, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 2. Therefore, it is rejected for the same reason. Regarding claim 12, recites a system comprises of a computer readable storage medium that is configured to perform the method of claim 5. Therefore, it is rejected for the same reason. Regarding claim 16, recites a system that is configured to perform the method of claim 2. Therefore, it is rejected for the same reason. Regarding claim 19, recites a system that is configured to perform the method of claim 5. Therefore, it is rejected for the same reason. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Quan-Zhen Wang can be reached at 571-272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ZHEN Y WU/Primary Examiner, Art Unit 2685 Application/Control Number: 18/406,872 Page 2 Art Unit: 2685 Application/Control Number: 18/406,872 Page 3 Art Unit: 2685 Application/Control Number: 18/406,872 Page 4 Art Unit: 2685 Application/Control Number: 18/406,872 Page 5 Art Unit: 2685 Application/Control Number: 18/406,872 Page 6 Art Unit: 2685 Application/Control Number: 18/406,872 Page 7 Art Unit: 2685 Application/Control Number: 18/406,872 Page 8 Art Unit: 2685 Application/Control Number: 18/406,872 Page 9 Art Unit: 2685 Application/Control Number: 18/406,872 Page 10 Art Unit: 2685 Application/Control Number: 18/406,872 Page 11 Art Unit: 2685 Application/Control Number: 18/406,872 Page 12 Art Unit: 2685
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