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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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) 1-6, 8, 10-13, 15 and 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuxing Wu et. al., Experimental and finite element modelling evaluation of cement integrity under diametric compression, Journal of Petroleum Science and Engineering, Volume 188, 2020, 106844, ISSN 0920-4105, https://doi.org/10.1016/j.petrol.2019.106844 (“Wu”), in view of US 11481894 to Wang.
Regarding Claim 1, Wu discloses a method for measuring set cement mechanical properties based on an image recognition technology (Figs. 1-3, Digital image correlation (DIC) technique to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study), wherein the method comprises: acquiring a first image of a set cement specimen taken by a photographing apparatus, the first image being an image of the set cement specimen not subjected to a compressive load in a compressive test (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study), extracting at least one feature point in the first image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images of speckled surface created by spraying black paint on a white painted surface recorded for calculation of strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study); acquiring a second image of the set cement specimen taken by the photographing apparatus, the second image being an image of the set cement specimen subjected to a compressive load during the compressive test (Figs. 1-3, Digital image correlation (DIC) technique with camera to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study), establishing an image relationship of the same feature point in the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software with speckled surface created by spraying black paint on a white painted surface for tracking of all grids in the system; Abstract, §2. Methodology, §2.1. Experimental study), determining a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study, §2.2. Numerical model setup); and determining a strain tensor by the deformation gradient (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study, §2.2. Numerical model setup).
However, although Wu discloses a Young's modulus parameter and a Poisson's ratio parameter, Wu does not explicitly disclose establishing an image grayscale relationship of the same feature point in the first image and the second image; and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor. Wang discloses establishing an image grayscale relationship of the same feature point in the first image and the second image (Figs. 1-6, grayscale gradient matrix of the image at each level; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26); determining a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-6, calculating deformation gradient F; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26); determining a strain tensor by the deformation gradient (Figs. 1-6, calculating Green-Lagrange strain tensor; Col. 11, line 47 – Col. 13, line 28); and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor (Figs. 1-6, calculating Young's modulus parameter and a Poisson's ratio; Col. 13, lines 32-59). It would have been obvious to one of ordinary skill in the art before the effective filing of the application to modify the invention of Wu by providing establishing an image grayscale relationship of the same feature point in the first image and the second image; and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor as in Wang in order to provide for greater accuracy in determining the deformation.
Regarding Claim 2, Wu discloses after acquiring the first image of the set cement specimen, and before extracting the at least one feature point in the first image, the method further comprises: extracting at least one recognizable region in the first image, each recognizable region comprising at least one feature point (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images of speckled surface created by spraying black paint on a white painted surface recorded for calculation of strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study).
Regarding Claim 3, Wang discloses establishing the image grayscale relationship of the same feature point in the first image and the second image comprises:
I(x,y,t) = I(x+u,y+v,t+Δt) wherein l represents a gray scale, in unit of %; x represents an x-axis coordinate of the feature point in the first image, in unit of mm; y is a y-axis coordinate of the feature point in the first image, in unit of mm; t is the photographing time of the first image, in unit of s; u is a variation amount of the x-axis coordinate of the feature point at a certain moment during the compressive test, in unit of mm; v is a variation amount of the y-axis coordinate of the feature point at a certain moment during the compressive test, in unit of mm; and Δt is the time difference between the moment at which the second image is acquired and the moment at which the first image is acquired, in unit of s (Figs. 1-6, grayscale gradient matrix of the image at each level; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26).
Regarding Claim 4, Wang discloses determining the deformation gradient of the feature point by the positions of the same feature point in the first image and the second image comprises: FX=∂X′/∂X wherein F.sub.X is the deformation gradient of the feature point, dimensionless; X is the coordinate of the feature point before deformation, in unit of mm; and X′ is the coordinate of the feature point after deformation, in unit of mm (Figs. 1-6, calculating deformation gradient F; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26).
Regarding Claim 5, Wang discloses determining the strain tensor by the deformation gradient, comprises: ε = 1/2(FXT x FX -I) wherein ε is the strain tensor, dimensionless; F.sub.X is the deformation gradient of the feature point, dimensionless; F.sub.X.sup.T is the transposed matrix of Fx, dimensionless; and/is an identity matrix, dimensionless (Figs. 1-6, calculating Green-Lagrange strain tensor; Col. 11, line 47 – Col. 13, line 28).
Regarding Claim 6, Wang discloses determining the Young's modulus parameter and the Poisson's ratio parameter by the strain tensor comprises: calculating an axial stress to which the set cement specimen is subjected:
σ =F/S ; calculating the Young's modulus parameter of the set cement specimen:
E=σ/εzz; and calculating the Poisson's ratio parameter of the set cement specimen:
v=-εzz/εH; wherein σ is the axial stress to which the set cement specimen is subjected, in unit of MPa; F is an axial force to which the set cement specimen is subjected, in unit of N, and is obtained by a detection device; S is the cross-sectional area of the set cement specimen, in unit of mm.sup.2; E is the Young's modulus of the set cement specimen, in unit of GPa; ε.sub.zs is axial strain of the set cement specimen, determined by axial strain data in the strain tensor, dimensionless; v is the Poisson's ratio of the set cement specimen, dimensionless; and ε.sub.H is hoop strain of the set cement specimen, determined by radial strain data in the strain tensor, dimensionless (Figs. 1-6, calculating Young's modulus parameter and a Poisson's ratio; Col. 13, lines 32-59).
Regarding Claim 8, Wu discloses an apparatus for measuring set cement mechanical properties based on an image recognition technology (Figs. 1-3, Digital image correlation (DIC) technique to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study), wherein the apparatus comprises a detection device, and detection device comprising: an experimental section, configured to acquire a first image and a second image of a set cement specimen (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study), the first image being an image of the set cement specimen not subjected to a compressive load in a compressive test, the second image being an image of the set cement specimen subjected to a compressive load during the compressive test (Figs. 1-3, Digital image correlation (DIC) technique to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study); a data analysis processing section, configured to: extract at least one feature point in the first image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software with speckled surface created by spraying black paint on a white painted surface; Abstract, §2. Methodology, §2.1. Experimental study); determine a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software with speckled surface created by spraying black paint on a white painted surface for tracking of all grids in the system; Abstract, §2. Methodology, §2.1. Experimental study); determine a strain tensor by the deformation gradient (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study, §2.2. Numerical model setup).
However, although Wu discloses a Young's modulus parameter and a Poisson's ratio parameter, Wu does not explicitly disclose determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor. Wang discloses determining a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-6, calculating deformation gradient F; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26); determining a strain tensor by the deformation gradient (Figs. 1-6, calculating Green-Lagrange strain tensor; Col. 11, line 47 – Col. 13, line 28); and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor (Figs. 1-6, calculating Young's modulus parameter and a Poisson's ratio; Col. 13, lines 32-59). It would have been obvious to one of ordinary skill in the art before the effective filing of the application to modify the invention of Wu by providing establishing an image grayscale relationship of the same feature point in the first image and the second image; and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor as in Wang in order to provide for greater accuracy in determining the material properties.
Regarding Claim 10, Wu discloses the experimental section comprises: a compressive strength testing machine (4), configured to apply a load to the set cement specimen (2) (Figs. 1-3, diametrical compressive load applied to sample; Abstract, §2. Methodology, §2.1. Experimental study, §2.2.2. Material properties and boundary conditions, Table 1); a platen (3), mounted on a loading shaft of the compressive strength testing machine (4) for transmitting a load of the compressive strength testing machine (4) (Figs. 1-3, load platen; Abstract, §2. Methodology, §2.1. Experimental study); and a photographing apparatus (6), mounted on the compressive strength testing machine (4) for taking the first image and the second image of the set cement specimen (2) (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study).
Regarding Claim 11, Wu discloses the data analysis processing section comprises: a compressive strength testing machine controller (7), electrically connected to the compressive strength testing machine (4) for controlling load applying of the compressive strength testing machine (4) on the set cement specimen (2) (Figs. 1-3, diametrical compressive load applied to sample at 0.1 mm/min of loading rate; Abstract, §2. Methodology, §2.1. Experimental study, §2.2.2. Material properties and boundary conditions, Table 1); a photographing controller (8), electrically connected to the photographing apparatus (6) for controlling a photographing action of the photographing apparatus (6) on the set cement specimen (2) (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study)., and receiving the first image and the second image taken by the photographing apparatus (6); and an image collector (9), configured to receive and process the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study)..
Regarding Claim 12, Wu discloses the experimental section further comprises: a pad (1), mounted on the compressive strength testing machine (4) and below the platen (3) for placing the set cement specimen (2) (Figs. 1-3, diametrical compressive load applied to sample between platens at 0.1 mm/min of loading rate; Abstract, §2. Methodology, §2.1. Experimental study, §2.2.2. Material properties and boundary conditions, Table 1).
Regarding Claim 13, Wang in Fig. 6 appears to disclose a plurality of photographing apparatuses (6) are provided, and the plurality of photographing apparatuses (6) are evenly installed around the set cement specimen (2) (Fig. 6, three evenly spaced image collection devices 30; Col. 14, lines 36-43).
Regarding Claim 15, Wu discloses electronic device (Figs. 1-3, Digital image correlation (DIC) photogrammetry system using DIC software; Abstract, §2. Methodology, §2.1. Experimental study), comprising: one or more processors (Figs. 1-3, Digital image correlation (DIC) photogrammetry system using DIC software; Abstract, §2. Methodology, §2.1. Experimental study); a storage device, configured to store one or more programs (Figs. 1-3, Digital image correlation (DIC) photogrammetry system using DIC software; Abstract, §2. Methodology, §2.1. Experimental study); wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method for measuring set cement mechanical properties based on an image recognition technology (Figs. 1-3, Digital image correlation (DIC) technique to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study), wherein the method comprises: acquiring a first image of a set cement specimen taken by a photographing apparatus, the first image being an image of the set cement specimen not subjected to a compressive load in a compressive test (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with camera with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study); extracting at least one feature point in the first image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images of speckled surface created by spraying black paint on a white painted surface recorded for calculation of strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study); acquiring a second image of the set cement specimen taken by the photographing apparatus, the second image being an image of the set cement specimen subjected to a compressive load during the compressive test (Figs. 1-3, Digital image correlation (DIC) technique with camera to monitor the strain distribution on the surface by comparing the images after and before deformation; Abstract, §2. Methodology, §2.1. Experimental study); establishing an image relationship of the same feature point in the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software with speckled surface created by spraying black paint on a white painted surface for tracking of all grids in the system; Abstract, §2. Methodology, §2.1. Experimental study), and determining a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study, §2.2. Numerical model setup); determining a strain tensor by the deformation gradient (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images recorded for calculation of the strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study, §2.2. Numerical model setup).
However, although Wu discloses a Young's modulus parameter and a Poisson's ratio parameter, Wu does not explicitly disclose establishing an image grayscale relationship of the same feature point in the first image and the second image; and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor. Wang discloses establishing an image grayscale relationship of the same feature point in the first image and the second image (Figs. 1-6, grayscale gradient matrix of the image at each level; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26); determining a deformation gradient of the feature point by positions of the same feature point in the first image and the second image (Figs. 1-6, calculating deformation gradient F; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26); determining a strain tensor by the deformation gradient (Figs. 1-6, calculating Green-Lagrange strain tensor; Col. 11, line 47 – Col. 13, line 28); and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor (Figs. 1-6, calculating Young's modulus parameter and a Poisson's ratio; Col. 13, lines 32-59). It would have been obvious to one of ordinary skill in the art before the effective filing of the application to modify the invention of Wu by providing establishing an image grayscale relationship of the same feature point in the first image and the second image; and determining a Young's modulus parameter and a Poisson's ratio parameter by the strain tensor as in Wang in order to provide for greater accuracy in determining the deformation.
Regarding Claim 17, Wu discloses after acquiring the first image of the set cement specimen, and before extracting the at least one feature point in the first image, the method further comprises: extracting at least one recognizable region in the first image, each recognizable region comprising at least one feature point (Figs. 1-3, Digital image correlation (DIC) photogrammetry system with images of speckled surface created by spraying black paint on a white painted surface recorded for calculation of strain map using DIC software; Abstract, §2. Methodology, §2.1. Experimental study).
Regarding Claim 18, Wang discloses establishing the image grayscale relationship of the same feature point in the first image and the second image comprises:
I(x,y,t) = I(x+u,y+v,t+Δt) wherein l represents a gray scale, in unit of %; x represents an x-axis coordinate of the feature point in the first image, in unit of mm; y is a y-axis coordinate of the feature point in the first image, in unit of mm; t is the photographing time of the first image, in unit of s; u is a variation amount of the x-axis coordinate of the feature point at a certain moment during the compressive test, in unit of mm; v is a variation amount of the y-axis coordinate of the feature point at a certain moment during the compressive test, in unit of mm; and Δt is the time difference between the moment at which the second image is acquired and the moment at which the first image is acquired, in unit of s (Figs. 1-6, grayscale gradient matrix of the image at each level; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26).
Regarding Claim 19, Wang discloses determining the deformation gradient of the feature point by the positions of the same feature point in the first image and the second image comprises: FX=∂X′/∂X wherein F.sub.X is the deformation gradient of the feature point, dimensionless; X is the coordinate of the feature point before deformation, in unit of mm; and X′ is the coordinate of the feature point after deformation, in unit of mm (Figs. 1-6, calculating deformation gradient F; Col. 6, lines 4-56, Col. 9, line 17- Col.11, line 26).
Regarding Claim 20, Wang discloses determining the strain tensor by the deformation gradient, comprises: ε = 1/2(FXT x FX -I) wherein ε is the strain tensor, dimensionless; F.sub.X is the deformation gradient of the feature point, dimensionless; F.sub.X.sup.T is the transposed matrix of Fx, dimensionless; and/is an identity matrix, dimensionless (Figs. 1-6, calculating Green-Lagrange strain tensor; Col. 11, line 47 – Col. 13, line 28).
Regarding Claim 21, Wang discloses determining the Young's modulus parameter and the Poisson's ratio parameter by the strain tensor comprises: calculating an axial stress to which the set cement specimen is subjected:
σ =F/S ; calculating the Young's modulus parameter of the set cement specimen:
E=σ/εzz; and calculating the Poisson's ratio parameter of the set cement specimen:
v=-εzz/εH; wherein σ is the axial stress to which the set cement specimen is subjected, in unit of MPa; F is an axial force to which the set cement specimen is subjected, in unit of N, and is obtained by a detection device; S is the cross-sectional area of the set cement specimen, in unit of mm.sup.2; E is the Young's modulus of the set cement specimen, in unit of GPa; ε.sub.zs is axial strain of the set cement specimen, determined by axial strain data in the strain tensor, dimensionless; v is the Poisson's ratio of the set cement specimen, dimensionless; and ε.sub.H is hoop strain of the set cement specimen, determined by radial strain data in the strain tensor, dimensionless (Figs. 1-6, calculating Young's modulus parameter and a Poisson's ratio; Col. 13, lines 32-59).
Claim(s) 7 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Wang as applied to claims 1 and 15, and further in view of US 20200333318 to Benkley.
Regarding Claims 7 and 22, Wu in view of Wang discloses the method according to claim 1 and the electronic device according to claim 15, but do not explicitly disclose the method further comprises: acquiring pressure data and time data of the set cement specimen subjected to the compressive load by the detection device, and calculating the compressive strength of the set cement specimen by the following formula: σ1=F1/S1 wherein σ1 is the compressive strength of the set cement specimen, in unit of MPa; F1 is the axial pressure when the set cement specimen is broken, in unit of N; and S1 is the cross-sectional area of the set cement specimen, in unit of mm.sup.2. Benkley discloses acquiring pressure data and time data of the set cement specimen subjected to the compressive load by the detection device, and calculating the compressive strength of the set cement specimen by the following formula: σ1=F1/S1 wherein σ1 is the compressive strength of the set cement specimen, in unit of MPa; F1 is the axial pressure when the set cement specimen is broken, in unit of N; and S1 is the cross-sectional area of the set cement specimen, in unit of mm.sup.2 (¶¶ [0037]-[0038]). It would have been obvious to one of ordinary skill in the art before the effective filing of the application to modify the invention of Wu in view of Wang by providing acquiring pressure data and time data of the set cement specimen subjected to the compressive load by the detection device, and calculating the compressive strength of the set cement specimen by the following formula: σ1=F1/S1 wherein σ1 is the compressive strength of the set cement specimen, in unit of MPa; F1 is the axial pressure when the set cement specimen is broken, in unit of N; and S1 is the cross-sectional area of the set cement specimen, in unit of mm.sup.2as in Benkley in order to provide for greater accuracy in determining material properties.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Wang and further in view of US 20200264082 to Shao.
Regarding Claim 14, Wu in view of Wang discloses the apparatus according to claim 13, but is silent regarding the experimental section further comprises: a plurality of glass plates (5), detachably mounted on the compressive strength testing machine (4) and between the set cement specimen (2) and the corresponding photographing apparatuses (6). Shao the experimental section further comprises: a plurality of glass plates (5), detachably mounted on the compressive strength testing machine (4) and between the set cement specimen (2) and the corresponding photographing apparatuses (6) (Figs. 1-3, tempered glass 11 between specimen in image pressure cell and camera fixed on camera bracket 15; ¶¶ [0028]-[0031], Claim 4; Note that plural glass plate corresponding to each camera is an obvious design choice). It would have been obvious to one of ordinary skill in the art before the effective filing of the application to modify the invention of Wu in view of Wang by providing the experimental section further comprises: a plurality of glass plates (5), detachably mounted on the compressive strength testing machine (4) and between the set cement specimen (2) and the corresponding photographing apparatuses (6) as in Shao in order to provide for pressure sealing the cell.
Conclusion
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/DAVID J BOLDUC/Primary Examiner, Art Unit 2852