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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1 - 5, 8 -13, 15, 17, 19, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over RINGDAHL (US-20240085186-A1) in view of Rohaly (US-20240247930-A1) in view of Kadambi (US-20210264147-A1) and in further view of Yun ( Tae Sup Yun, Kwang Yeom Kim, Jinhyun Choo, Dong Hun Kang, Quantifying the distribution of paste-void spacing of hardened cement paste using X-ray computed tomography, Materials Characterization, Volume 73, 2012,Pages 137-143,ISSN 1044-5803, https://doi.org/10.1016/j.matchar.2012.08.008.(https://www.sciencedirect.com/science/article/pii/S1044580312002367).
Regarding Claim 1, Ringdahl teaches a computer-implemented method comprising: receiving a plurality of images of the surface; reconstructing the plurality of images into at least one three-dimensional representation of the surface (Para 43-46: describes receiving a plurality of images of earth surface and reconstructing the images into at least one three-dimensional representation of the surface).
Ringdahl fails to teach wherein the plurality of images comprise images that are each captured under a different lighting direction relative to the other images and fails to further teach feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for air void identification, wherein the algorithm identifies the air voids on the surface.
Rohaly teaches wherein the plurality of images comprise images that are each captured under a different lighting direction relative to the other images (Para.83 and para 96: Explains multiple images being captured under different lighting directions. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl to incorporate teachings of Rohaly by allowing the system to capture multiple images under different lighting directions. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Rohaly fails to teach feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for defect identification, wherein the algorithm identifies the defects on the surface and detecting air voids specifically ).
Kadambi teaches feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for defect identification, wherein the algorithm identifies the defects on the surface (Para.14 -18: describes feeding a three-dimensional surface model into a trained statistical model, including a convolutional neural network or classifier, to detect surface defects. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly to incorporate teachings of Kadambi by allowing the system to feed the 3d representation of the surface into a trained algorithm to identify defects. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Kadambi fails to teach detecting air voids specifically.
Yun teaches detecting air voids in surfaces (Section 2.3 and fig. 1-3: describes methods for identifying and analyzing air voids in hardened cement-based materials using X-ray CT imaging. The CT scans are used to reconstruct a 3D voxel model of mortar samples, and automated image processing is applied to detect and quantify air voids by size volume and spacing). It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly in further view of Kadambi to incorporate teachings of Yun by allowing the system to be able to detect air voids as one of the defects in the 3d surface representation. This combination would enhance the detection system allowing the system to detect air voids in concrete material which would allow the user to address quality issues).
Regarding claim 2, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, wherein the surface comprises a hardened concrete surface (Yun, Section 2.1-2.3: describes the material being analyzed is a hardened cement-based surface such as mortar or concrete).
Regarding claim 3, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, wherein the method occurs without modifying the contrasts of the plurality of images of the surface (Ringdahl, Para.43-46:mention use of captured camera images directly for 3D reconstruction).
Regarding claim 5, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, further comprising a step of capturing the plurality of images (Ringdahl. Para 43-46: describes receiving a plurality of images of earth surface and reconstructing the images into at least one three-dimensional representation of the surface).
Regarding claim 8, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, wherein the algorithm is a machine learning algorithm, wherein the machine learning algorithm is trained to distinguish between air voids and non-air voids on the surface (Kadambi, Para 14-18: describes a machine learning algorithm such as CNN trained to detect defects in a 3D surface. While Kadambi does not explicitly describe classification of air voids, Yun, section 2.3: describes identifying and quantifying air voids in hardened cement-based surfaces using image segmentation and 3D. “Segmentation of air voids from the cement matrix is performed by image thresholding (i.e., binarization). Given the varied distribution of pixel values in each 2D image, the Otsu's method”) It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Kadambi to incorporate teachings of Yun by allowing the system to be able to detect air voids as one of the defects in the 3d surface representation and distinguish between air voids and non-air voids . This combination would enhance the detection accuracy of the system when identifying air voids).
Regarding claim 9, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 8, wherein the machine learning algorithm comprises a Convolutional Neural Network (CNN) algorithm (Kadambi, Para.17: describes the use of convolutional neural network to detect surface defects.) .
Regarding claim 10, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, wherein the identifying of the air voids comprises segmenting the air voids (Yun, Section 2.3, fig 1.b: describes segmenting air voids from cement matrix using thresholding and binarization).
Regarding claim 11, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, further comprising a step of displaying the resulting air void identification (Yun, fig, 1.b and fig.2 display results of void detection).
Regarding claim 12, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 1, further comprising a step of utilizing the identification results to assess the quality of the surface (Yun, abstract, Section.1 para 2 and section 4.3: describes obtaining identification results regarding size, spacing, and distribution of air voids in cement-based materials using x-ray CT and segmentation. These results are then used to assess the quality of the surface based on standards such as ASTM C457 and correlate with durability indicators like freeze thaw).
Regarding claim 13, Ringdahl in view of Rohaly, Kadambi and in further view of Yun teaches the method of claim 12, wherein the quality of the surface comprises a free-thaw performance of the surface (Yun, Abstract, table.3 and section 4.3: describes assessing the quality of a surface based on identification of air voids using CT imaging. Yun further discloses that the identification results can be used as a basis for evaluating freeze-that performance).
Regarding claim 15, Ringdahl teaches A computing device comprises one or more computer readable storage mediums having a program code embodied therewith, wherein the computing device comprises a processor operable to execute the program code, (Para.21 computer readable storage medium), and wherein the program code comprises programming instructions for:receiving a plurality of images of the surface; reconstructing the received images into at least one three-dimensional representation of the surface (Para 43-46: describes receiving a plurality of images of earth surface and reconstructing the images into at least one three-dimensional representation of the surface).
Ringdahl fails to teach wherein the plurality of images comprise images that are each captured under a different lighting direction relative to the other images and fails to further teach feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for air void identification, wherein the algorithm identifies the air voids on the surface.
Rohaly teaches wherein the plurality of images comprise images that are each captured under a different lighting direction relative to the other images (Para.83 and para 96: Explains multiple images being captured under different lighting directions. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl to incorporate teachings of Rohaly by allowing the system to capture multiple images under different lighting directions. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Rohaly fails to teach feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for defect identification, wherein the algorithm identifies the defects on the surface and detecting air voids specifically ).
Kadambi teaches feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for defect identification, wherein the algorithm identifies the defects on the surface (Para.14 -18: describes feeding a three-dimensional surface model into a trained statistical model, including a convolutional neural network or classifier, to detect surface defects. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly to incorporate teachings of Kadambi by allowing the system to feed the 3d representation of the surface into a trained algorithm to identify defects. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Kadambi fails to teach detecting air voids specifically.
Yun teaches detecting air voids in surfaces (Section 2.3 and fig. 1-3: describes methods for identifying and analyzing air voids in hardened cement-based materials using X-ray CT imaging. The CT scans are used to reconstruct a 3D voxel model of mortar samples, and automated image processing is applied to detect and quantify air voids by size volume and spacing). It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly in further view of Kadambi to incorporate teachings of Yun by allowing the system to be able to detect air voids as one of the defects in the 3d surface representation. This combination would enhance the detection system allowing the system to detect air voids in concrete material which would allow the user to address quality issues).
Regarding Claim 17, it falls under the same rejection as claim 12 because it is similar in scope and dependent upon the same references.
Regarding Claim 19, it falls under the same rejection as claim 5 because it is similar in scope and dependent upon the same references.
Regarding Claim 21, it falls under the same rejection as claim 8 because it is similar in scope and dependent upon the same references.
Regarding Claim 22, it falls under the same rejection as claim 9 because it is similar in scope and dependent upon the same references.
Regarding claim 23, Ringdahl teaches A system , wherein the system comprises:a hardware system comprising: a camera operable to capture a plurality of images of the surface, and a processor operable to reconstruct the received images into a three-dimensional representation of the surface (Para 43-46: describes receiving a plurality of images of earth surface and reconstructing the images into at least one three-dimensional representation of the surface).
Ringdahl fails to teach capturing 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 further fails to teach wherein the software system comprises an algorithm specifically trained for air void identification, wherein the algorithm is operational to receive the reconstructed three-dimensional representation of the plurality of images from the hardware system and identify the air voids.
Rohaly teaches wherein the plurality of images comprise images that are each captured under a different lighting direction relative to the other images (Para.83 and para 96: Explains multiple images being captured under different lighting directions. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl to incorporate teachings of Rohaly by allowing the system to capture multiple images under different lighting directions. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Rohaly fails to teach wherein the software system comprises an algorithm specifically trained for air void identification, wherein the algorithm is operational to receive the reconstructed three-dimensional representation of the plurality of images from the hardware system and identify the air voids.
Kadambi teaches feeding the reconstructed three-dimensional representation of the surface into an algorithm specifically trained for defect identification, wherein the algorithm identifies the defects on the surface (Para.14 -18: describes feeding a three-dimensional surface model into a trained statistical model, including a convolutional neural network or classifier, to detect surface defects. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly to incorporate teachings of Kadambi by allowing the system to feed the 3d representation of the surface into a trained algorithm to identify defects. This combination would enhance the final output quality and detect any issues in the reconstruction of the 3d surface representation). Kadambi fails to teach detecting air voids specifically.
Yun teaches detecting air voids in surfaces (Section 2.3 and fig. 1-3: describes methods for identifying and analyzing air voids in hardened cement-based materials using X-ray CT imaging. The CT scans are used to reconstruct a 3D voxel model of mortar samples, and automated image processing is applied to detect and quantify air voids by size volume and spacing). It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly and Kadambi to incorporate teachings of Yun by allowing the system to be able to detect air voids as one of the defects in the 3d surface representation. This combination would enhance the detection of the system by allowing it to detect air voids in concrete material which would allow the user to address quality issues).
Regarding claim 24, Ringdahl in view of Rohaly, Kadambi and Yun teaches the system of 23, further comprising a graphical user interface in electrical communication with the algorithm, wherein the graphical user interface is operable to display the resulting air void identification (Yun, fig, 1.b and fig.2 display results of air void detection).
Regarding claim 25, Ringdahl in view of Rohaly, Kadambi and Yun teaches the system of 23, wherein the algorithm is a machine learning algorithm, wherein the machine learning algorithm is trained to distinguish between air voids and non-air voids on the surface (Kadambi, Para 14-18: describes a machine learning algorithm such as CNN trained to detect defects in a 3D surface. While Kadambi does not explicitly describe classification of air voids, Yun, section 2.3: describes identifying and quantifying air voids in hardened cement-based surfaces using image segmentation and 3D. “Segmentation of air voids from the cement matrix is performed by image thresholding (i.e., binarization). Given the varied distribution of pixel values in each 2D image, the Otsu's method”).
Claim(s) 6-7, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over RINGDAHL (US-20240085186-A1) in view of Rohaly, Kadambi, Yun and in further view of Putman (US-20210241478-A1).
Regarding claim 6, Ringdahl in view of Kadambi, Yun and Putman teaches the method of claim 1, wherein the reconstructing of the images occurs by three-dimensional photometric reconstruction (Putman. Para.4: describes capturing a plurality of images of a specimen surface using a plurality of light sources positioned at different angles and positions relative to the specimen. Para.03: describes the use of photometric stereo techniques to estimate surface orientation from images captured under different illumination directions.Para.17 and para.19: also describes using photometric stereo technique for 3D reconstruction. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly, Kadambi and Yun to incorporate teachings of Putman by allowing the system to be able to use the technic of photometric stereo reconstruction. This combination would enhance the 3d surface reconstruction accuracy)
Regarding claim 7, Ringdahl in view of Rohaly, Kadambi, Yun and in further view of Putman teaches the method of claim 1, wherein the reconstructing of the images occurs through the use of a hardware system, wherein the hardware system comprises:a camera operable to capture the 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 the three-dimensional representation (Ringdahl. Para 43-46: describes receiving a plurality of images of earth surface and reconstructing the images into at least one three-dimensional representation of the surface. Putman, Para. 04:describes a camera and multiple light sources capturing images from different light directions. Para.17 and para. 19: describes using a deep learning model to reconstruct a 3d surface).
Regarding Claim 16, it falls under the same rejection as claim 7 because it is similar in scope and dependent upon the same references.
Regarding Claim 20, it falls under the same rejection as claim 6 because it is similar in scope and dependent upon the same references.
Claim(s) 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over RINGDAHL (US-20240085186-A1) in view of Kadambi, Rohaly, Yun, and in further view of Chen (US-20160002108-A1).
Regarding claim 14, Ringdahl in view of Rohaly , Kadambi and Yun teaches the method of claim 1, but fails to teach further comprising a step of utilizing the identification results to recommend a surface treatment decision, implement the surface treatment decision, or combinations thereof.
Chen teaches utilizing the identification results to recommend a surface treatment decision, implement the surface treatment decision, or combinations thereof (Para.84: describes evaluating the air void structure of concrete and applying a treatment formula (e.g air entraining agents and fatty acids) to achieve desirable air void characteristics. It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the system of Ringdahl in view of Rohaly, Kadambi and Yun to incorporate teachings of Chen by allowing the system to be able to come up with a surface treatment for the air voids. This combination would enhance the 3D surface reconstruction by producing higher quality results).
Regarding Claim 18, it falls under the same rejection as claim 14 because it is similar in scope and dependent upon the same references.
Response to Arguments
Applicant's arguments filed 08/08/2025, with respect to the rejection(s) of claims 1 -3, 5, 8 -13, 15, 17, 19, and 21-22 are rejected under 35 U.S.C. 103 have been fully considered and are persuasive in light of the amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Ringdahl in view of Rohaly , Kadambi and Yun as fully explained above.
Applicant's arguments filed 08/08/2025, with respect to the Objection of claim 15 under Claim Objections have been fully considered and are persuasive in light of the amendments to the claims. Therefore, the objection has been withdrawn.
Conclusion
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/LATRELL ANTHONY CREARY/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613