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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202410454118.5, filed on 04/16/2024.
Claim Objections
Applicant is advised that should claim 19 be found allowable, claim 20 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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) 1-4, 8-11, and 15-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (CN 117148340 A).
Regarding claim 1, Li discloses
A geological disasters monitoring method (see pg. 1, “The invention relates to the technical field of geological disaster detection,”), comprising:
determining microscopic deformation parameters of an area to be monitored according to remote sensing observation data corresponding to a slope body of the area to be monitored (see pg. 3, “This invention obtains millimeter- to meter-level deformation information by processing different satellite remote sensing images,…The location and boundaries of active geological hazards are automatically delineated, liberating the current situation of manual delineation, greatly improving the efficiency of delineating the location and range of active geological hazards, and qualitatively combining the average slope of active geological hazards”; see pg. 2, “The average slope within the range of the active geological hazard is collected, and the average slope is used to classify the active geological hazard into active landslides or ground subsidence.”);
determining macroscopic deformation parameters of the area to be monitored according to optical remote sensing data and terrain data of the area to be monitored (see pg. 4, “Step S211: Use GAMMA software to generate an interference pattern using Sentinel-1 satellite remote sensing images and auxiliary data (precision orbit, external DEM, etc.).”; see Fig. 2b, terrain data and satellite image); and
determining the landslide remote sensing geomechanical deformation type in the area to be monitored according to material composition, movement mode, slope structure, the microscopic deformation parameters and the macroscopic deformation parameters of the area to be monitored (see pg. 3, “A qualitative module is used to collect the average slope within the range of the active geological hazard, and use the average slope to classify the active geological hazard into active landslides or ground subsidence.”; see Figs. 8 and 9).
Regarding claim 2, Li further discloses
The geological disasters monitoring method according to claim 1, wherein the determining microscopic deformation parameters of an area to be monitored according to remote sensing observation data corresponding to a slope body of the area to be monitored comprises:
in a case that the slope body of the area to be monitored is an east-west slope body, using Interferometric Synthetic Aperture Radar (InSAR) technology to invert a time sequence microscopic deformation process of a landslide body according to time sequence data of radar satellite images, and determining microscopic deformation data according to the time sequence microscopic deformation process, wherein the microscopic deformation data comprises a microscopic deformation magnitude and a deformation area (see pg. 1, “Combined with Enhanced InSAR Stacking technology, SAR POT, OPOT and DEM differential technology to process the satellite remote sensing images to obtain millimeter to meter level deformation information;”; see pg. 2, Enhanced InSAR Stacking process steps, “Adopt the strategy of InSAR stacking and atmospheric correction algorithm to reduce the estimation bias caused by non-stationary signals in satellite remote sensing images; Generate interferograms using satellite remote sensing images and auxiliary data after reducing the estimation bias; Suppress random noise in the interference pattern through adaptive filtering; The filtered interferogram is phase unwrapped using the minimum cost flow algorithm MCF; Correct the atmospheric delay in the interferogram after phase unwrapping, and use the least squares method to estimate the average velocity of each point in the interferogram after correcting phase unwrapping to obtain the final surface deformation information.”); and
in a case that the slope body of the area to be monitored is a north-south slope body, using Pixel offset tracking (POT) technology to acquire the microscopic deformation data of the area to be monitored according to optical image time sequence data (see pg. 2, “Preferably, using SAR POT and OPOT technology to process the satellite remote sensing images includes the following steps: The SAR POT and OPOT calculate the correlation between synthetic aperture radar image blocks in the search window, track moving targets in multi-temporal images, capture the entire pixel and sub-pixel level offset, and obtain the final surface deformation information.”).
Regarding claim 3, Li further discloses
The geological disasters monitoring method according to claim 2, wherein the determining microscopic deformation data according to the time sequence microscopic deformation process comprises:
extracting an average deformation velocity and an accumulated deformation amount from the time sequence microscopic deformation process (see pgs. 4-5, “Step S214: Correct the atmospheric delay in the interferogram after phase unwrapping to increase the possibility of random residual noise, and use the least squares method to estimate the average velocity of each point in the interferogram after correcting phase unwrapping. Step S22: Use SAR POT and OPOT technology to process satellite remote sensing images, that is, by calculating the correlation between satellite remote sensing image blocks in the search window, tracking moving targets in multi-temporal images and capturing the entire pixels and sub-pixels level offset to obtain the final surface deformation information.”);
determining an average deformation rate according to the deformation velocity and the accumulated deformation amount (see Figs. 4a and 4b, annual deformation rate; pg. 6, “It can be seen from the figure that the mean and standard deviation of the millimeter to meter level deformation results obtained by this embodiment are within the acceptable range, indicating that the millimeter to meter level deformation results obtained by this embodiment are reliable.”); and
determining the microscopic deformation magnitude according to the average deformation rate (see pg. 1, “Optimize and process the satellite remote sensing images to obtain millimeter to meter level deformation information; Combined with Enhanced InSAR Stacking technology, SAR POT, OPOT and DEM differential technology to process the satellite remote sensing images to obtain millimeter to meter level deformation information; Use the MCD method to statistically identify moving pixels in millimeter to meter level deformation information;”).
Regarding claim 4, Li further discloses
The geological disasters monitoring method according to claim 2, wherein when the average deformation rate is more than 100mm/a, the microscopic deformation magnitude is a large-scale microscopic deformation, when the average deformation rate is more than 50mm/a and less than or equal to 100mm/a, the microscopic deformation magnitude is a medium-scale microscopic deformation, and when the average deformation rate is less than or equal to 50mm/a, the microscopic deformation magnitude is a small-scale microscopic deformation (see pg. 5, there can be different deformation rates, “Step S52: When the active landslide speed V⟨100mm/yr, the active landslide is classified as a first-level landslide. Step S53: When the active landslide speed V⟩100mm/yr, the active landslide is classified as a secondary landslide.”).
Regarding claim 8, Li further discloses
A computer device comprising a memory, a processor and a computer program which is stored in the memory and operable on the processor, wherein the processor executes the computer program to implement steps of the geological disasters monitoring method according to claim 1 (see pg. 3, “The present invention also provides an active geological disaster detection system based on multi-source earth observation, including: The image acquisition module is used to acquire several satellite remote sensing images; optimize the satellite remote sensing images to obtain millimeter to meter level deformation information; The information processing module uses the MCD method to statistically identify motion pixels in millimeter to meter level deformation information; The detection module uses the DBSCAN method to process the moving pixels, delineate the location and boundaries of active geological hazards, and obtain the range of active geological hazards; A qualitative module is used to collect the average slope within the range of the active geological hazard, and use the average slope to classify the active geological hazard into active landslides or ground subsidence.”).
Regarding claims 9-11, the same cited sections and rationale for claims 2-4 are applied.
Regarding claim 15, the same cited sections and rationale from claim 8 are applied.
Regarding claims 16-18, the same cited sections and rationale for claims 2-4 are applied.
Allowable Subject Matter
None of the prior art of record teach or suggest the subject matter of dependent claims 5, 7, 12, 14, 19, and 20. The prior art of record does not anticipate or render fairly obvious in combination to teach all of the additional limitations of the claimed invention, as best understood within the context of Applicant’s claimed invention as a whole.
Accordingly, claims 5, 7, 12, 14, 19, and 20 are deemed to have allowable subject matter. Claims 6 and 13 could also be considered allowable subject matter by virtue of their dependence on allowable claims.
Claims 5-7, 12-14, and 19-20 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.
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
CN 117031425 A (Li); The invention claims a method and a system for predicting instability time of large gradient landslide based on satellite-borne SAR, relating to the field of geological disaster prediction; The method comprises: obtaining the time sequence SAR image data and landslide basic data of the landslide area to be detected; obtaining the time sequence deformation rate data corresponding to each pixel point in the time sequence SAR image data; according to the current observation time sequence in the corresponding time range, and the time sequence target deformation speed data corresponding to the measuring point, through the inverse speed model under the model parameter, obtaining the prediction instability collapse time sequence corresponding to the measuring point; using the amplitude information of the satellite-borne SAR data to calculate the large gradient landslide deformation, predicting the landslide destabilization time through the inverse speed method, making up the blank of the traditional InSAR on the large gradient landslide prediction, providing guarantee for the disaster prevention of the large gradient landslide; at the same time, free SAR data can be stably obtained in the research area, which greatly reduces the monitoring cost and increases the reliability of prediction.
CN 117437559 A (Huo); The invention claims a method and device for detecting surface rock movement deformation of coal mine area based on unmanned aerial vehicle, the method comprises: using the unmanned aerial vehicle flying platform to collect the flying remote sensing image in the working area to be tested, obtaining the unmanned aerial vehicle flying image data of the target terrain of the working area to be tested; collecting the satellite remote sensing observation data of the work area to be tested, wherein the satellite remote sensing observation data comprises SAR radar image data and optical satellite image data; based on the unmanned aerial vehicle flight image data and the optical satellite image data, calculating the first surface deformation information; calculating the second deformation information based on the SAR radar image data and the optical satellite image data; combining the first surface deformation information and the second surface deformation information to construct a geologic model of the surface deformation; evaluating the surface change of the working area to be tested by using the geologic model of the surface deformation. The method improves the earth surface deformation detection precision.
US 20250035816 A1 (Feng); The present invention discloses a method for dynamically assessing slope safety, and the method comprises the following steps: S1, carrying out geologic model generalization to the slope according to slope type, surface elevation, slope structure, stratum characteristics and a deformation failure mode to obtain a slope geologic model, creating a slope geometric model according to the said slope geologic model, carrying out the subdivision of computational grid, and selecting a reasonable numerical simulation method, mechanical constitutive and initial boundary value conditions to form a computational model; and S2, adjusting stratum parameters, structural plane parameters and activating factor strength based on the said computational model, carrying out a large amount of numerical simulation, summarizing results of the said numerical simulation, normalizing input quantities and output quantities to establish machine learning samples.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISABELLA A EDRADA whose telephone number is (571)272-4859. The examiner can normally be reached Mon - Fri 9am-5pm ET.
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/ISABELLA A EDRADA/Examiner, Art Unit 3648
/BERNARR E GREGORY/Primary Examiner, Art Unit 3648
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