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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/05/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-4 and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. US PG-Pub(US 20230220770 A1) in view Wang et al. US PG-Pub(US 20230401790 A1).
Regarding Claim 1, Yamada teaches a method for generating a segmented image of cuttings particles(¶[0023] FIG. 12 is a flow diagram of a method of using the analysis and control system of FIG. 4,), the method comprising:acquiring and preparing the cuttings particles for imaging(¶[0038], “the rock particles may be prepared for analysis by, for example, drying the rock particles in an oven for analysis (e.g., sample preparation 88).”, ¶[0038] discloses a preprocessing step of drying the rock particles before analysis.);placing the prepared cuttings particles in front of a digital camera(¶[0039], “ the tray 92 may be placed in front of a camera 52 and a photo 94 of the cuttings 46 may be taken (e.g., photo acquisition 96).”, rock particles are placed in front of a camera for imaging.);
and generating a segmented image identifying individual ones of the cuttings particles(¶[0045], “the embodiments described herein apply instance segmentation to extract individual cuttings 46 from a particular photo 94 (i.e., each individual cutting 46 is identified from a pile of cuttings 46 depicted in a particular photo 94). In other words, a plurality of individual pixels in a particular photo 94 that relate to a particular individual cutting 46 are identified as corresponding to that particular individual cutting 46, and may be analyzed together, as described in greater detail herein, to determine properties of the particular individual cutting 46. In addition, the embodiments described herein ascertain measurements of color distribution, grain size, size and shape (and other morphological properties), texture classification, and so forth, of individual cuttings 46 identified in photos 94.”, discloses segmenting the image to identify individual cuttings from the pile of cuttings.).
Yamada does not explicitly teach acquiring at least three digital images of the cuttings particles at corresponding non- coplanar illumination angles; combining the at least three digital images to generate a photometric stereo image of the cuttings particles;
Wang teaches acquiring at least three digital images of the cuttings particles at corresponding non- coplanar illumination angles([0110] “The 3D reconstruction of hardened concrete surface requires at least three images captured under various illumination directions with a fixed field-of-view. Applicant's product uses six LED lights, which were lighted up in sequence, to simulate the illuminations from different directions. After each illumination, the camera automatically took a picture of a concrete surface. There were six pictures captured by the camera during each sequence. The parameters of the camera, such as aperture, ISO, and shutter time, were fixed during the experimental process. FIGS. 6A-6F present the six images captured for one concrete sample.”, ¶[0110] discloses imaging the surface in various illumination directions which inherently would mean they are at different angles and positions.); combining the at least three digital images to generate a photometric stereo image of the cuttings particles (¶[0088], “Thereafter, camera 44 may capture a plurality of images of surface 42 at different light directions while the plurality of lights 46 sequentially illuminate the surface at different light directions during image capture. Thereafter, a processor reconstructs the received images into a three-dimensional representation of the surface. Next, an algorithm for air void identification that is in electrical communication with the hardware system receives the reconstructed three-dimensional representation of the surface to identify the air voids of the surface.”, discloses receiving the images captured and performing 3d construction to generate a reconstructed photometric stereo image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada with Wang in order to image the cuttings in different illumination conditions and generate a photometric stereo image based on combining the images. One skilled in the art would have been motivated to modify Yamada in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Regarding Claim 2, the combination of Yamada and Wang teach the method of claim 1, where Yamada further teaches wherein the acquiring and preparing further comprises: drilling a subterranean wellbore([0029] FIG. 1 illustrates a drilling operation 10 in accordance with the embodiments described herein. As illustrated, in certain embodiments, a drill string 12 may be suspended at an upper end by a kelly 12 and a traveling block 14 and terminated at a lower end by a drill bit ); collecting the cuttings particles from circulating drilling fluid(¶[0037] discloses collecting the cutting particles from a shale shaker.); washing the collected cuttings particles and rinsing the washed cuttings particles. (¶[0038] discloses the rock particles are dried in an oven before analysis which means they would have to be washed and rinsed beforehand.)
Regarding Claim 3, the combination of Yamada and Wang teach the method of claim 1, where Yamada teaches wherein: the placing comprises placing the prepared drill cuttings particles on a rotatable stage in front of the digital camera([0039] Then, in certain embodiments, the tray 92 may be placed in front of a camera 52 and a photo 94 of the cuttings 46 may be taken (e.g., photo acquisition 96), particles are placed on a tray in front of a camera.);
Wang teaches the acquiring comprises rotating the rotatable stage to at least three distinct angular orientations and acquiring a digital image of the cuttings particles at each of the at least three distinct angular orientations. (¶[0006],”Additional embodiments of the present disclosure pertain to a system for automated identification of air voids on a surface. In some embodiments, the system includes a hardware system containing a camera operable to capture a plurality of images of the surface at different light directions, a plurality of lights operable to sequentially illuminate the surface at different light directions during the capture of the plurality of images, and a processor operable to reconstruct the received images into a three-dimensional representation of the surface.”, ¶[0006] discloses imaging the object of interest in different orientation and in ¶[0101] “The six LED (Light Emitting Diode) lights are from Smart Vision Lights Inc. and the model is LM75. This LED light can provide a wide-angle uniform light projection, and can simulate the parallel light emitted from a point light source at an infinite distance. The six LED lights are fixed in a 16 cm diameter circle with equal intervals and the tilt angle of each LED light is 45° which is shown in FIG. 2B.” disclose the tilt angle of each LED light is around 45 degrees.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada with Wang in order to image the cuttings in different illumination conditions. One skilled in the art would have been motivated to modify Yamada in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Regarding Claim 4, the combination of Yamada and Wang teach the method of claim 1, where Wang further teaches wherein: the placing comprises placing the prepared drill cuttings particles on a stage in front of the digital camera and at least three light sources configured to illuminate the stage at correspondingly distinct angular orientations. (¶[0006],”Additional embodiments of the present disclosure pertain to a system for automated identification of air voids on a surface. In some embodiments, the system includes a hardware system containing a camera operable to capture a plurality of images of the surface at different light directions, a plurality of lights operable to sequentially illuminate the surface at different light directions during the capture of the plurality of images, and a processor operable to reconstruct the received images into a three-dimensional representation of the surface.”, ¶[0006] discloses imaging the object of interest in different orientation and in ¶[0101] “The six LED (Light Emitting Diode) lights are from Smart Vision Lights Inc. and the model is LM75. This LED light can provide a wide-angle uniform light projection, and can simulate the parallel light emitted from a point light source at an infinite distance. The six LED lights are fixed in a 16 cm diameter circle with equal intervals and the tilt angle of each LED light is 45° which is shown in FIG. 2B.” disclose the tilt angle of each LED light is around 45 degrees.); and the acquiring comprises selectively and individually illuminating each of the at least three light sources and acquiring a digital image of the cuttings particles corresponding to each of the individual illuminations of the at least three light sources. (¶[0042], “the plurality of images include at least three images of the surface. In some embodiments, each of the plurality of images are captured under different lighting directions. In some embodiments, each of the plurality of images are captured under a fixed field of view.”, discloses the at least 3 images are imaged under different lighting directions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada with Wang in order to image the cuttings in different illumination conditions. One skilled in the art would have been motivated to modify Yamada in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Regarding Claim 10, the combination of Yamada and Wang teach the method of claim 1, where Yamada further teaches wherein the acquiring, the combining, and the generating are performed automatically. (¶[0031] “the analysis and control system 50 may be configured to automatically analyze photos of the drill bit cuttings 46 that are automatically captured by one or more cameras 52 during performance of the drilling operation 10 illustrated in FIG. 1,”, discloses the analysis is done automatically)
Regarding Claim 11, the combination of Yamada and Wang teach the method of claim 1, where Yamada further teaches further comprising estimating a characteristic of a subterranean formation from the segmented image. (¶[0044], “In contrast, the embodiments described herein apply object-based image analysis to focus the classification on an object of interest (e.g., depicted in a photo 94) where lithological information resides. Although the embodiments described herein are described mainly in terms of cutting image lithology recognition and measurements, the techniques described herein may also be extend to the analysis of images of outcrops (e.g., beds, laminate, heterogeneities, and so forth), cores (e.g., depending on the relative bed angles, and so forth, as long as the acquisition steps are performed under controlled conditions.”, ¶[0044] discloses classifying the particles and determining certain characteristics during the analysis.)
Regarding Claim 12, claim 12 is considered an apparatus claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Yamada teaches a system for generating a segmented image of cuttings particles (Fig. 5) comprising: a sample holder(See ¶[0038] a tray holds the samples) a digital camera(See, ¶[0031]), a light source (See Figure 5 a camera is coupled to a light source and further disclosed in ¶[0031]) and a controller([0033] In certain embodiments, the one or more processors 58 may include a microprocessor, a microcontroller, a processor module or subsystem)
Regarding Claim 13, the combination of Yamada and Wang teach the system of claim 12, where Wang further teaches wherein the controller is further configured to rotate the sample holder to at least three distinct angular orientations corresponding to the non-coplanar illumination angles when acquiring the at least three digital images of the cuttings particles. (¶[0006],”Additional embodiments of the present disclosure pertain to a system for automated identification of air voids on a surface. In some embodiments, the system includes a hardware system containing a camera operable to capture a plurality of images of the surface at different light directions, a plurality of lights operable to sequentially illuminate the surface at different light directions during the capture of the plurality of images, and a processor operable to reconstruct the received images into a three-dimensional representation of the surface.”, ¶[0006] discloses imaging the object of interest in different orientation and in ¶[0101] “The six LED (Light Emitting Diode) lights are from Smart Vision Lights Inc. and the model is LM75. This LED light can provide a wide-angle uniform light projection, and can simulate the parallel light emitted from a point light source at an infinite distance. The six LED lights are fixed in a 16 cm diameter circle with equal intervals and the tilt angle of each LED light is 45° which is shown in FIG. 2B.” disclose the tilt angle of each LED light is around 45 degrees.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada with Wang in order to image the cuttings in different illumination conditions. One skilled in the art would have been motivated to modify Yamada in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. US PG-Pub(US 20230220770 A1) in view Wang et al. US PG-Pub(US 20230401790 A1) in view of Julia et al. ("Shape-based image segmentation through photometric stereo").
Regarding Claim 5, while the combination of Yamada and Wang teach the method of claim 1, they do not explicitly teach wherein: the combining comprises computing surface normal vectors i and albedo reflectivity at selected pixels in the photometric stereo image; and the generating the segmented image further comprises identifying individual ones of the cuttings particles based on the computed surface normal vectors.
Julia teaches wherein: the combining comprises computing surface normal vectors i and albedo reflectivity at selected pixels in the photometric stereo image (Page 93, Left Col, Paragraphs 1-3, “This paper assumes a Lambertian reflectance model, which states that materials absorb and reflect light uniformly in all directions. wherer(u,v) is the albedo at pixel(u,v), n(u,v) is its surface normal and m represents the light direction associated with each image. The albedo describes the fraction of light reflected at each point on the object.”, discloses determining surface normal vectors based on the reflectivity of the pixels in the photometric stereo image. )and the generating the segmented image further comprises identifying individual ones of the cuttings particles based on the computed surface normal vectors. (Abstract, “the proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering.”, discloses generating a segmented image by using the estimated normal vectors through clustering.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada and Wang with Julia in order to compute the normal vectors of the pixels in the image. One skilled in the art would have been motivated to modify Yamada and Wang in this manner in order to segment the objects or surfaces present in the scene. (Julia, Abstract)
Regarding Claim 14, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claim 6 are rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. US PG-Pub(US 20230220770 A1) in view Wang et al. US PG-Pub(US 20230401790 A1) in view of McQueen US PG-Pub(US 20200134851 A1).
Regarding Claim 6, while the combination of Yamada and Wang teach the method of claim 1, they do not explicitly teach wherein the generating the segmented image further comprises: detecting and extracting shadows from the at least three digital images; estimating directions of selected ones of the extracted shadows; and identifying individual ones of the cuttings particles based on the estimated shadow directions.
McQueen teaches wherein the generating the segmented image further comprises: detecting and extracting shadows from the at least three digital images; (¶[0058], “Color components of the image are selected to obtain distinct object shadow information.”, discloses detecting and obtaining shadow information from the images) estimating directions of selected ones of the extracted shadows([0068] “In the next processing step, represented in FIG. 9 in a view 900, a shadow direction vector D is calculated based upon the known location 510 of the red source (as projected onto the reference surface) and the centroid 520 of the previously-found item base shadow image 360.”, ¶[0068] discloses calculating a shadow vector of the extracted shadow); and identifying individual ones of the cuttings particles based on the estimated shadow directions. (¶[0058], “An image of the dark areas is collected under the objects' bases from the color camera below the reference surface. The image of object bases is put through a process of thresholding to enhance the location and shape of the object bases. Thresholding of the image allows individual objects to be discerned or segmented”, discloses shadow information is used to determine individual objects to be segmented in the image. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada and Wang with McQueen in order to use shadow information to identify an object. One skilled in the art would have been motivated to modify Yamada and Wang in this manner in order to determine the location, dimension, and height of the object. (McQueen, Abstract)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. US PG-Pub(US 20230220770 A1) in view Wang et al. US PG-Pub(US 20230401790 A1) in view of Julia et al. ("Shape-based image segmentation through photometric stereo") in view of McQueen US PG-Pub(US 20200134851 A1)
Regarding Claim 7, while the combination of Yamada and Wang teach the method of claim 1, they do not explicitly teach wherein the generating the segmented image further comprises: generating a first segmented image in which individual ones of the cuttings particles are identified based on computed surface normal vectors in the photometric stereo image; and generating a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images.
Julia teaches wherein the generating the segmented image further comprises: generating a first segmented image in which individual ones of the cuttings particles are identified based on computed surface normal vectors in the photometric stereo image (Abstract, “the proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering.”, discloses generating a segmented image by using the estimated normal vectors through clustering.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada and Wang with Julia in order to compute the normal vectors of the pixels in the image. One skilled in the art would have been motivated to modify Yamada and Wang in this manner in order to segment the objects or surfaces present in the scene. (Julia, Abstract)
However, the combination of Yamada, Wang and Julia do not explicitly teach generating a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images.
McQueen teaches generating a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images. (¶[0058], “Color components of the image are selected to obtain distinct object shadow information.”, discloses detecting and obtaining shadow information from the images and creating a generated segmented image. ([0068] “In the next processing step, represented in FIG. 9 in a view 900, a shadow direction vector D is calculated based upon the known location 510 of the red source (as projected onto the reference surface) and the centroid 520 of the previously-found item base shadow image 360.”, ¶[0068] discloses calculating a shadow vector of the extracted shadow);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang and Julia with McQueen in order to use shadow information to identify an object. One skilled in the art would have been motivated to modify Yamada, Wang and Julia in this manner in order to determine the location, dimension, and height of the object. (McQueen, Abstract)
Claims 8-9 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. US PG-Pub(US 20230220770 A1) in view Wang et al. US PG-Pub(US 20230401790 A1) in view of Julia et al. ("Shape-based image segmentation through photometric stereo") in view of McQueen US PG-Pub(US 20200134851 A1) in view of Hagiopol US PG-Pub(US 20200372659 A1)
Regarding Claim 8, while the combination of Yamada, Wang, Julia and McQueen teach the method of claim 7, they do not explicitly teach wherein the generating the segmented image further comprises combining the first segmented image and the second segmented image to obtain a third segmented image.
Hagiopol teaches wherein the generating the segmented image further comprises combining the first segmented image and the second segmented image to obtain a third segmented image. ([0013] “FIG. 3 illustrates combining two image segmentations of an image produced by different methods and refining the combined segmentation into a more accurate refined segmentation of the image.”, discloses combining two segmented images to create a more refined third image. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang, Julia and McQueen with Hagiopol in order to combine the first and second segmented images. One skilled in the art would have been motivated to modify Yamada, Wang, Julia and McQueen in this manner in order to produce an improved or more accurate resulting segmentation. (Hagiopol, ¶[0045])
Regarding Claim 9, the combination of Yamada, Wang, Julia, McQueen and Hagiopol teach the method of claim 8, where Yamada further teaches wherein the generating the segmented image further comprises applying edge detection techniques or region growing techniques to the third segmented image. (¶[0053], “An automated cutting description is, therefore, possible with objective measurements with relatively high precision using the object-based image analysis and quantitative photographic acquisition of the workflow 118. The example illustrated in FIG. 9 shows how the clustering phase makes it possible to set boundaries, even between clusters that are gradually changing (e.g., fine-medium sand”, discloses setting boundaries and performing edge detection.)
Regarding Claim 15, the combination of Yamada, Wang and Julia teach the system of claim 14,
where Julia further teaches wherein the generate the segmented image further comprises: generate a first segmented image in which individual ones of the cuttings particles are identified based on computed surface normal vectors in the photometric stereo image; (Abstract, “the proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering.”, discloses generating a segmented image by using the estimated normal vectors through clustering.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada and Wang with Julia in order to compute the normal vectors of the pixels in the image. One skilled in the art would have been motivated to modify Yamada and Wang in this manner in order to segment the objects or surfaces present in the scene. (Julia, Abstract)
Yamada, Wang and Julia do not explicitly teach generate a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images
McQueen teaches generate a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images (¶[0058], “Color components of the image are selected to obtain distinct object shadow information.”, discloses detecting and obtaining shadow information from the images and creating a generated segmented image. ([0068] “In the next processing step, represented in FIG. 9 in a view 900, a shadow direction vector D is calculated based upon the known location 510 of the red source (as projected onto the reference surface) and the centroid 520 of the previously-found item base shadow image 360.”, ¶[0068] discloses calculating a shadow vector of the extracted shadow);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang and Julia with McQueen in order to use shadow information to identify an object. One skilled in the art would have been motivated to modify Yamada, Wang and Julia in this manner in order to determine the location, dimension, and height of the object. (McQueen, Abstract)
However, the combination of Yamada, Wang, Julia and McQueen do not explicitly teach combining the first segmented image and the second segmented image to obtain a third segmented image.
Hagiopol teaches combining the first segmented image and the second segmented image to obtain a third segmented image. ([0013] “FIG. 3 illustrates combining two image segmentations of an image produced by different methods and refining the combined segmentation into a more accurate refined segmentation of the image.”, discloses combining two segmented images to create a more refined third image. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang, Julia and McQueen with Hagiopol in order to combine the first and second segmented images. One skilled in the art would have been motivated to modify Yamada, Wang, Julia and McQueen in this manner in order to produce an improved or more accurate resulting segmentation. (Hagiopol, ¶[0045])
Regarding Claim 16, Yamada teaches a method for generating a segmented image of cuttings particles(¶[0023] FIG. 12 is a flow diagram of a method of using the analysis and control system of FIG. 4,), the method comprising:acquiring and preparing the cuttings particles for imaging(¶[0038], “the rock particles may be prepared for analysis by, for example, drying the rock particles in an oven for analysis (e.g., sample preparation 88).”, ¶[0038] discloses a preprocessing step of drying the rock particles before analysis.);placing the prepared cuttings particles in front of a digital camera(¶[0039], “ the tray 92 may be placed in front of a camera 52 and a photo 94 of the cuttings 46 may be taken (e.g., photo acquisition 96).”, rock particles are placed in front of a camera for imaging.);
and generating a segmented image identifying individual ones of the cuttings particles(¶[0045], “the embodiments described herein apply instance segmentation to extract individual cuttings 46 from a particular photo 94 (i.e., each individual cutting 46 is identified from a pile of cuttings 46 depicted in a particular photo 94). In other words, a plurality of individual pixels in a particular photo 94 that relate to a particular individual cutting 46 are identified as corresponding to that particular individual cutting 46, and may be analyzed together, as described in greater detail herein, to determine properties of the particular individual cutting 46. In addition, the embodiments described herein ascertain measurements of color distribution, grain size, size and shape (and other morphological properties), texture classification, and so forth, of individual cuttings 46 identified in photos 94.”, discloses segmenting the image to identify individual cuttings from the pile of cuttings.).
Yamada does not explicitly teach acquiring at least three digital images of the cuttings particles at corresponding non- coplanar illumination angles; combining the at least three digital images to generate a photometric stereo image of the cuttings particles;
Wang teaches acquiring at least three digital images of the cuttings particles at corresponding non- coplanar illumination angles([0110] “The 3D reconstruction of hardened concrete surface requires at least three images captured under various illumination directions with a fixed field-of-view. Applicant's product uses six LED lights, which were lighted up in sequence, to simulate the illuminations from different directions. After each illumination, the camera automatically took a picture of a concrete surface. There were six pictures captured by the camera during each sequence. The parameters of the camera, such as aperture, ISO, and shutter time, were fixed during the experimental process. FIGS. 6A-6F present the six images captured for one concrete sample.”, ¶[0110] discloses imaging the surface in various illumination directions which inherently would mean they are at different angles and positions.); combining the at least three digital images to generate a photometric stereo image of the cuttings particles (¶[0088], “Thereafter, camera 44 may capture a plurality of images of surface 42 at different light directions while the plurality of lights 46 sequentially illuminate the surface at different light directions during image capture. Thereafter, a processor reconstructs the received images into a three-dimensional representation of the surface. Next, an algorithm for air void identification that is in electrical communication with the hardware system receives the reconstructed three-dimensional representation of the surface to identify the air voids of the surface.”, discloses receiving the images captured and performing 3d construction to generate a reconstructed photometric stereo image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada with Wang in order to image the cuttings in different illumination conditions and generate a photometric stereo image based on combining the images. One skilled in the art would have been motivated to modify Yamada in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Yamada and Wang do not explicitly teach generating a first segmented image in which individual ones of the cuttings particles are identified based on computed surface normal vectors in the photometric stereo image
Julia teaches generating a first segmented image in which individual ones of the cuttings particles are identified based on computed surface normal vectors in the photometric stereo image (Abstract, “the proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering.”, discloses generating a segmented image by using the estimated normal vectors through clustering.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada and Wang with Julia in order to compute the normal vectors of the pixels in the image. One skilled in the art would have been motivated to modify Yamada and Wang in this manner in order to segment the objects or surfaces present in the scene. (Julia, Abstract)
Yamada, Wang and Julia do not explicitly teach generating a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images.
McQueen teaches generating a second segmented image in which individual ones of the cuttings particles are identified from estimated directions of shadows extracted from the at least three digital images. (¶[0058], “Color components of the image are selected to obtain distinct object shadow information.”, discloses detecting and obtaining shadow information from the images and creating a generated segmented image. ([0068] “In the next processing step, represented in FIG. 9 in a view 900, a shadow direction vector D is calculated based upon the known location 510 of the red source (as projected onto the reference surface) and the centroid 520 of the previously-found item base shadow image 360.”, ¶[0068] discloses calculating a shadow vector of the extracted shadow);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang and Julia with McQueen in order to use shadow information to identify an object. One skilled in the art would have been motivated to modify Yamada, Wang and Julia in this manner in order to determine the location, dimension, and height of the object. (McQueen, Abstract)
However, the combination of Yamada, Wang, Julia and McQueen do not explicitly teach combining the first segmented image and the second segmented image to obtain a third segmented image.
Hagiopol teaches combining the first segmented image and the second segmented image to obtain a third segmented image. ([0013] “FIG. 3 illustrates combining two image segmentations of an image produced by different methods and refining the combined segmentation into a more accurate refined segmentation of the image.”, discloses combining two segmented images to create a more refined third image. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Wang, Julia and McQueen with Hagiopol in order to combine the first and second segmented images. One skilled in the art would have been motivated to modify Yamada, Wang, Julia and McQueen in this manner in order to produce an improved or more accurate resulting segmentation. (Hagiopol, ¶[0045])
Regarding Claim 17, the combination of Yamada, Wang, Julia, McQueen and Hagiopol teach The method of claim 16, where Yamada further teaches wherein the acquiring and preparing further comprises: drilling a subterranean wellbore([0029] FIG. 1 illustrates a drilling operation 10 in accordance with the embodiments described herein. As illustrated, in certain embodiments, a drill string 12 may be suspended at an upper end by a kelly 12 and a traveling block 14 and terminated at a lower end by a drill bit ); collecting the cuttings particles from circulating drilling fluid(¶[0037] discloses collecting the cutting particles from a shale shaker.); washing the collected cuttings particles and rinsing the washed cuttings particles. (¶[0038] discloses the rock particles are dried in an oven before analysis which means they would have to be washed and rinsed beforehand.)
Regarding Claim 18, the combination of Yamada, Wang, Julia, McQueen and Hagiopol teach the method of claim 16, where Yamada teaches wherein: the placing comprises placing the prepared drill cuttings particles on a rotatable stage in front of the digital camera([0039] Then, in certain embodiments, the tray 92 may be placed in front of a camera 52 and a photo 94 of the cuttings 46 may be taken (e.g., photo acquisition 96), particles are placed on a tray in front of a camera.);
Wang teaches the acquiring comprises rotating the rotatable stage to at least three distinct angular orientations and acquiring a digital image of the cuttings particles at each of the at least three distinct angular orientations. (¶[0006],”Additional embodiments of the present disclosure pertain to a system for automated identification of air voids on a surface. In some embodiments, the system includes a hardware system containing a camera operable to capture a plurality of images of the surface at different light directions, a plurality of lights operable to sequentially illuminate the surface at different light directions during the capture of the plurality of images, and a processor operable to reconstruct the received images into a three-dimensional representation of the surface.”, ¶[0006] discloses imaging the object of interest in different orientation and in ¶[0101] “The six LED (Light Emitting Diode) lights are from Smart Vision Lights Inc. and the model is LM75. This LED light can provide a wide-angle uniform light projection, and can simulate the parallel light emitted from a point light source at an infinite distance. The six LED lights are fixed in a 16 cm diameter circle with equal intervals and the tilt angle of each LED light is 45° which is shown in FIG. 2B.” disclose the tilt angle of each LED light is around 45 degrees.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yamada, Julia, McQueen and Hagiopol with Wang in order to image the cuttings in different illumination conditions. One skilled in the art would have been motivated to modify Yamada, Julia, McQueen and Hagiopol in this manner in order for automated identification of air voids on a surface, such as a hardened concrete surface. (Wang, ¶[0003])
Regarding Claim 19, the combination of Yamada, Wang, Julia, McQueen and Hagiopol teach the method of claim 16, where Yamada further teaches wherein the generating the segmented image further comprises applying edge detection techniques or region growing techniques to the third segmented image. (¶[0053], “An automated cutting description is, therefore, possible with objective measurements with relatively high precision using the object-based image analysis and quantitative photographic acquisition of the workflow 118. The example illustrated in FIG. 9 shows how the clustering phase makes it possible to set boundaries, even between clusters that are gradually changing (e.g., fine-medium sand”, discloses setting boundaries and performing edge detection.)
Regarding Claim 20, the combination of Yamada, Wang, Julia, McQueen and Hagiopol teach the method of claim 16, where Yamada further teaches further comprising estimating a characteristic of a subterranean formation from the third segmented image. (¶[0044], “In contrast, the embodiments described herein apply object-based image analysis to focus the classification on an object of interest (e.g., depicted in a photo 94) where lithological information resides. Although the embodiments described herein are described mainly in terms of cutting image lithology recognition and measurements, the techniques described herein may also be extend to the analysis of images of outcrops (e.g., beds, laminate, heterogeneities, and so forth), cores (e.g., depending on the relative bed angles, and so forth, as long as the acquisition steps are performed under controlled conditions.”, ¶[0044] discloses classifying the particles and determining certain characteristics during the analysis.)
Conclusion
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/HAN HOANG/Primary Examiner, Art Unit 2661
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