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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-6, are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant has not pointed out where the amended claim 1 is supported, nor does there appear to be a written description of the claim limitation “wherein the color information is used to refine the 3D points in space” (claim 2), “wherein refining the 3D points in space comprises: identifying one or more 3D points in space associated with captured features that have a low confidence grade; and changing a status of the one or more 3D points associated with the captured features identified as having the low confidence grade” (claim 3), “wherein the one or more processors are further configured to: determine respective confidence grades for one or more captured features that are associated with one or more 3D points in space; and assign the respective confidence grades to the one or more captured features” (claim 4) , “wherein the one or more 3D points associated with the captured features identified as having a low confidence grade are not used to generate the digital 3D representation of the intraoral object” (claim 5), and “wherein the one or more 3D points associated with the captured features are weighted based on the respective confidence grades” (claim 6) in the original disclosure as filed.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 7, 12-17 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Mark S. Quadling et al. [US 20080101688 A1; and incorporated by reference Henley Quadling et al. (US 7184150 B2) and "Multiple View Geometry in Computer Vision" by Richard Hartley and Andrew Zisserman, Second Edition] in view of Henrik Öjelund et al. [US 20210106409 A1].
Regarding claim 1, Mark teaches:
1. (Currently amended) An intraoral scanning system (i.e. FIG. 5 is a dental system in which the 3D photogrammetry technique of this disclosure may be implemented- ¶0014… A intra-oral laser digitizer system provides a three-dimensional visual image of a real-world object such as a dental item through a laser digitization- Abstract, Henley) comprising:
an elongate handheld wand with a probe (i.e. The imaging system may include a small image sensor and objective lens mounted directly at the end of the sensor probe to provide a smaller intra-oral probe through elimination of the relay lenses- col 12, line 57-65) at a distal end (i.e. The enclosure may be a hand-held enclosure or have a form factor suitable for being handheld, for enclosing the electrical circuit 112 and for manipulating the intra-oral digitizer 100 in vivo- Col 5, line 62-65, Henley);
a structured light pattern projector configured to project a structured light pattern onto an intraoral object (i.e. A pattern typically comprises a plurality of curves, with each curve being substantially parallel to one another- ¶0019), the structured light pattern projector comprising a light source configured to transmit light (i.e. a projecting coupling lens system 205, a laser or other light source system 20- ¶0036, fig. 6);
a plurality of cameras configured to capture a plurality of images of the intraoral object, wherein at least a subset of the plurality of images comprises captured features of the structured light pattern projected onto the intraoral object by the structured light projectors, and wherein one or more images of the plurality of images comprise color information (i.e. An alternative method may be used when a two camera system is employed. In this case, a dense laser pattern is projected onto the object to be digitized. Two images are obtained from two different positions, simultaneously. In this case, the denser the laser pattern, the better. Application/Control Number: 19/238,333 Page 7 Art Unit: 2488 The laser also has the effect of producing a laser speckle pattern, such that, in addition to the intended illuminated pattern on the object, other blobs of reflected light are visible. In the case of a two camera system where the two images are taken simultaneously, this laser speckle pattern has the added benefit of providing additional feature points on the surface that are now imaged from two cameras- ¶0033-36, figs. 2-4); and
one or more processors, configured to
determine, from the plurality of images, a correspondence between projected features of the structured light pattern generated by the structured light pattern projector and captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object; use the determined correspondence and the color information to determine three- dimensional (3D) points in space associated with the captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object (i.e. For each position M (where M goes from 1 through N), as part of the 3D computation, one may determine a function FM(x,y,z)[Wingdings font/0xE0](a,b), where (x, y, z) is a real world 3D coordinate, and (a, b) is a pixel coordinate. Thus, for each position, a 3D coordinate may be mapped back to a corresponding pixel location on the image sensor- ¶0026…Matching pixels between different positions (i.e., for different values of M) are then determined. This can be done by examining each pixel in the set SM, and finding corresponding pixels in another set SQ where M≠Q. There may be more than one set with a matching pixel. Well-known photogrammetry operations, such as cross-correlation, may be used to determine matching pixels. To aid in the search process, the system may take advantage of the fact that each pixel in SM also corresponds to a 3D coordinate in CM, and that FM may be applied to that coordinate to obtain an initial guess for a neighborhood in SM for searching. For example, if (a, b) is a pixel in SM, a corresponding 3D coordinate (x, y, z) is related to the pixel. FQ(x, y, z)=(c, d) produces a new pixel for position Q. For all subsequent searches in SQ to determine correspondence, the computations may then be restricted to a neighborhood of (c, d), thus significantly reducing the computational load for determining correspondence- ¶0028… Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a Application/Control Number: 19/238,333 Page 8 Art Unit: 2488 bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030); and
generate a digital 3D representation of the intraoral object based on the determined 3D points in space (i.e. A structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object. The structured light digitizing operation generates a 3D model of the object being scanned, and this model is then used to compute a higher accuracy model using photogrammetry- Abstract… In a representative method, the use of a structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object- ¶0007).
However, Mark does not teach explicitly:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element.
In the same field of endeavor, Henrik teaches:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment Application/Control Number: 19/238,333 Page 11 Art Unit: 2488 of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130 (a line in a true cross-sectional view, but shown here at an angle for clarity)… FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 2, Mark and Henrik teach all the limitations of claim 1.
However, Mark does not teach explicitly:
wherein the color information is used to refine the 3D points in space.
In the same field of endeavor, Henrik teaches:
wherein the color information is used to refine the 3D points in space (i.e. In some embodiments, the surface data also comprises color information. Adding color information to the surface data may make the tissue type determination more secure- ¶0036).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 7, Mark and Henrik teach all the limitations of claim 1 and Mark further teaches:
wherein generating the digital 3D representation of the intraoral object is performed using a 3D reconstruction algorithm (i.e. These simultaneous left and right images, combined with knowledge of the projected pattern, are fed into the algorithms to compute the 3D data as described- ¶0036)
However, Mark does not teach explicitly:
wherein the structured light pattern comprises a checkboard pattern;
In the same field of endeavor, Henrik teaches:
wherein the structured light pattern comprises a checkboard pattern (i.e. FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 12, Mark and Henrik teach all the limitations of claim 1 and Mark further teaches:
wherein: the plurality of cameras comprises a first camera and a second camera; and the one or more processors are configured to determine the correspondence by determining agreement between the first camera and the second camera that the projected features of the projected structured light pattern captured by the first camera and the second camera are located at the 3D points in space (i.e. Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030).
Regarding claim 13, Mark and Henrik teach all the limitations of claim 12 and Mark further teaches:
wherein: each of the first camera and the second camera comprise a camera sensor that has an array of pixels, for each of which there exists a corresponding camera ray in 3D space originating from the pixel whose direction is towards the intraoral object being captured; andthe one or more processors are configured to determine agreement between the first camera and the second camera by determining, for each projector ray associated with the projected features of the projected structured light pattern captured by the first camera and the second camera, intersections with camera rays of the first camera and intersections with camera rays of the second camera (i.e. Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030).
Regarding claim 14, Mark and Henrik teach all the limitations of claim 13 and Mark further teaches:
wherein the first camera and the second camera agree when camera rays of the first camera and camera rays of the second camera intersect a given projector ray at the same 3D point in space (i.e. Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030).
Regarding claim 15, Mark and Henrik teach all the limitations of claim 1.
However, Mark does not teach explicitly:
wherein the pattern generating optical element comprises a transmission mask or a transparency mask.
In the same field of endeavor, Henrik teaches:
wherein the pattern generating optical element comprises a transmission mask or a transparency mask (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130 (a line in a true cross-sectional view, but shown here at an angle for clarity)… FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 16, Mark and Henrik teach all the limitations of claim 1.
However, Mark does not teach explicitly:
wherein the light source comprises a light emitting diode (LED).
In the same field of endeavor, Henrik teaches:
wherein the light source comprises a light emitting diode (LED) (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 17, Mark and Henrik teach all the limitations of claim 1.
However, Mark does not teach explicitly:
wherein the structured light pattern is an unchanging light pattern.
In the same field of endeavor, Henrik teaches:
wherein the structured light pattern is an unchanging light pattern (i.e. FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 20, Mark and Henrik teach all the limitations of claim 1 and Mark further teaches:
wherein the one or more processors are configured to use stored calibration values for each camera ray corresponding to each pixel of each camera sensor of the plurality of cameras and for each projector ray corresponding to each projected portion of the structured light pattern to determine the correspondence between the points in the structured light pattern generated by the structured light pattern projector and the captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object (i.e. Thus, for each position, a 3D coordinate may be mapped back to a corresponding pixel location on the image sensor. An example of such a function is the standard pinhole camera model, which may be applied to the imaging system using a standard lens calibration procedure to determine the image center (cx, cy) and focal length α- ¶0026… An alternative method may be used when a two camera system is employed. In this case, a dense laser pattern is projected onto the object to be digitized. Two images are obtained from two different positions, simultaneously. In this case, the denser the laser pattern, the better. The laser also has the effect of producing a laser speckle pattern, such that, in addition to the intended illuminated pattern on the object, other blobs of reflected light are visible. In the case of a two camera system where the two images are taken simultaneously, this laser speckle pattern has the added benefit of providing additional feature points on the surface that are now imaged from two cameras. These feature points may be used in a standard photogrammetry analysis between the two images to perform the computation using a known (and calibrated) distance between the two camera sensors to obtain the 3D coordinates. Knowledge of how the laser speckle pattern behaves on the material is question also may be used to adjust the measured speckle feature points prior to doing the photogrammetric computation- ¶0033).
Regarding claim 21, Mark and Henrik teach all the limitations of claim 20 and Mark further teaches:
wherein using the stored calibration values comprises: mapping all projector rays and all camera rays corresponding to all captured portions of the structured light pattern into three-dimensional space;identifying all intersections of at least one camera ray and at least one projector ray; and selecting a subset of all of the intersections based on agreement between two or more cameras of the plurality of cameras on respective camera rays of the two or more cameras intersecting with a same projector ray at approximately a same three-dimensional point in space (i.e. An additional performance improvement may be obtained by determining an epipolar line on the image for position Q, and searching along that portion of the epipolar line contained in the search neighborhood determined above. The epipolar geometry may be determined, for example, from intrinsic parameters for the camera, as well as from the extrinsic parameters obtained from the relevant alignment matrices Ti. The article "Multiple View Geometry in Computer Vision" by Richard Hartley and Andrew Zisserman, Second Edition, provides a detailed description of how to determine the epipolar line. This approach (incorporated by reference) allows a further improvement in the search for matching of features to occur in O(n) time as compared to O(n2) time- ¶0029).
Claims 8-9 and 22-30 are rejected under 35 U.S.C. 103 as being unpatentable over Mark S. Quadling et al. [US 20080101688 A1; and incorporated by reference Henley Quadling et al. (US 7184150 B2) and "Multiple View Geometry in Computer Vision" by Richard Hartley and Andrew Zisserman, Second Edition] in view of Henrik Öjelund et al. [US 20210106409 A1] and further in view of Feng Zhang et al. [US 20210212589 A1].
Regarding claim 8, Mark and Henrik teach all the limitations of claim 1.
However, Mark and Henrik do not teach explicitly:
wherein the one or more processors are further configured to:
use the color information to determine, for one or more of the captured features, whether it is projected onto fixed tissue or moving tissue, wherein the determination of whether the one or more captured features are projected onto fixed tissue or moving tissue is used in generating the digital 3D representation of the intraoral object.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
use the color information to determine, for one or more of the captured features, whether it is projected onto fixed tissue or moving tissue, wherein the determination of whether the one or more captured features are projected onto fixed tissue or moving tissue is used in generating the digital 3D representation of the intraoral object (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 9, Mark, Henrik and Feng teach all the limitations of claim 8.
However, Mark and Henrik do not teach explicitly:
wherein captured features determined to be projected onto moving tissue are not used for generating the digital 3D representation of the intraoral object.
In the same field of endeavor, Feng teaches:
wherein captured features determined to be projected onto moving tissue are not used for generating the digital 3D representation of the intraoral object (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133… if it is found that the subject moves during the scan, the processing device 140 may perform one or more operations to avoid or reduce motion artifacts, and reconstruct a corrected scanning image- ¶0084…the reconstructed image may be a two-dimensional (2D) image or a three-dimensional (3D) image- ¶0142).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 22, Mark teaches:
22. (New) An intraoral scanning system (i.e. FIG. 5 is a dental system in which the 3D photogrammetry technique of this disclosure may be implemented- ¶0014… A intra-oral laser digitizer system provides a three-dimensional visual image of a real-world object such as a dental item through a laser digitization- Abstract, Henley) comprising:
an elongate handheld wand with a probe (i.e. The imaging system may include a small image sensor and objective lens mounted directly at the end of the sensor probe to provide a smaller intra-oral probe through elimination of the relay lenses- col 12, line 57-65) at a distal end (i.e. The enclosure may be a hand-held enclosure or have a form factor suitable for being handheld, for enclosing the electrical circuit 112 and for manipulating the intra-oral digitizer 100 in vivo- Col 5, line 62-65, Henley);
a structured light pattern projector configured to project a structured light pattern onto an intraoral object (i.e. A pattern typically comprises a plurality of curves, with each curve being substantially parallel to one another- ¶0019), the structured light pattern projector comprising a light source configured to transmit light (i.e. a projecting coupling lens system 205, a laser or other light source system 20- ¶0036, fig. 6);
a plurality of cameras configured to capture features of the structured light pattern projected onto the intraoral object by the structured light pattern projector (i.e. An alternative method may be used when a two camera system is employed. In this case, a dense laser pattern is projected onto the object to be digitized. Two images are obtained from two different positions, simultaneously. In this case, the denser the laser pattern, the better. Application/Control Number: 19/238,333 Page 7 Art Unit: 2488 The laser also has the effect of producing a laser speckle pattern, such that, in addition to the intended illuminated pattern on the object, other blobs of reflected light are visible. In the case of a two camera system where the two images are taken simultaneously, this laser speckle pattern has the added benefit of providing additional feature points on the surface that are now imaged from two cameras- ¶0033-36, figs. 2-4); and
one or more processors configured to:
determine a correspondence between projected features of the structured light pattern generated by the structured light pattern projector and captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object; use the determined correspondence to determine three-dimensional (3D) points in space associated with the captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object (i.e. For each position M (where M goes from 1 through N), as part of the 3D computation, one may determine a function FM(x,y,z)[Wingdings font/0xE0](a,b), where (x, y, z) is a real world 3D coordinate, and (a, b) is a pixel coordinate. Thus, for each position, a 3D coordinate may be mapped back to a corresponding pixel location on the image sensor- ¶0026…Matching pixels between different positions (i.e., for different values of M) are then determined. This can be done by examining each pixel in the set SM, and finding corresponding pixels in another set SQ where M≠Q. There may be more than one set with a matching pixel. Well-known photogrammetry operations, such as cross-correlation, may be used to determine matching pixels. To aid in the search process, the system may take advantage of the fact that each pixel in SM also corresponds to a 3D coordinate in CM, and that FM may be applied to that coordinate to obtain an initial guess for a neighborhood in SM for searching. For example, if (a, b) is a pixel in SM, a corresponding 3D coordinate (x, y, z) is related to the pixel. FQ(x, y, z)=(c, d) produces a new pixel for position Q. For all subsequent searches in SQ to determine correspondence, the computations may then be restricted to a neighborhood of (c, d), thus significantly reducing the computational load for determining correspondence- ¶0028… Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a Application/Control Number: 19/238,333 Page 8 Art Unit: 2488 bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030); and
generate a digital 3D representation of the intraoral object based on the determined 3D points in space (i.e. A structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object. The structured light digitizing operation generates a 3D model of the object being scanned, and this model is then used to compute a higher accuracy model using photogrammetry- Abstract… In a representative method, the use of a structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object- ¶0007).
However, Mark does not teach explicitly:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element.
In the same field of endeavor, Henrik teaches:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment Application/Control Number: 19/238,333 Page 11 Art Unit: 2488 of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130 (a line in a true cross-sectional view, but shown here at an angle for clarity)… FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
However, Mark and Henrik do not teach explicitly:
wherein one or more captured features of the structured light pattern that fail to satisfy a criterion are removed from consideration for at least one of determining the correspondence or determining the 3D points in space.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
wherein one or more captured features of the structured light pattern that fail to satisfy a criterion are removed from consideration for at least one of determining the correspondence or determining the 3D points in space (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 23, Mark, Henrik and Feng teach all the limitations of claim 22 and Mark further teaches:
However, Mark and Henrik do not teach explicitly:
wherein the criterion comprises a fixed tissue criterion, and wherein the one or more processors are further configured to:determine that the one or more captured features were projected onto moving tissue, wherein the one or more captured features that were projected onto moving tissue fail to satisfy the fixed tissue criterion.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
wherein the criterion comprises a fixed tissue criterion, and wherein the one or more processors are further configured to:determine that the one or more captured features were projected onto moving tissue, wherein the one or more captured features that were projected onto moving tissue fail to satisfy the fixed tissue criterion (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 24, Mark, Henrik and Feng teach all the limitations of claim 22 and Mark further teaches:
However, Mark and Henrik do not teach explicitly:
wherein the plurality of cameras capture color information of the intraoral object, and wherein the color information is used to: determine whether the one or more captured features of the structured light pattern satisfy the criterion.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
wherein the criterion comprises a fixed tissue criterion, and wherein the one or more processors are further configured to: determine that the one or more captured features were projected onto moving tissue, wherein the one or more captured features that were projected onto moving tissue fail to satisfy the fixed tissue criterion (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133… The determination of the motion of the subject may refer to operation 610 described in FIG. 6. In some embodiments, the motion may include a relatively stable motion phase (e.g., the mid and late diastole, the expiratory phase, the eye opening phase) and a relatively unstable motion phase (e.g., the systole, the inspiratory phase, the eye blinking phase). A portion of the scanning data may correspond to the relatively stable motion phase of the motion, while another portion of the scanning data may correspond to the relatively unstable motion phase of the motion- ¶0140… In 704, the processing device 140 (e.g., the scanning module 508 of the processing device 140) may extract the portion of the scanning data corresponding to the stable motion phase of the motion. In some embodiments, the processing device 140 may determine the stable motion phase of the motion and determine a portion of the scanning data corresponding to the stable motion phase. The processing device 140 may then extract the portion of the scanning data. The extracted portion of the scanning data may be barely affected by the motion of the object- ¶0141… It should be understood that according to the reconstruction of the scanning image of the ROI using the scanning data acquired or obtained when the motion of the object is relatively stable, motion artifacts in the reconstructed image may be effectively reduced or removed- ¶0143).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 25, Mark, Henrik and Feng teach all the limitations of claim 22 and Mark further teaches:
However, Mark and Henrik do not teach explicitly:
wherein the criterion is an intensity threshold, and wherein the one or more processors are further configured to: determine respective intensities for one or more of the captured pattern features, wherein the one or more captured pattern features having an intensity that is below the intensity threshold fail to satisfy the criterion.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
wherein the criterion is an intensity threshold, and wherein the one or more processors are further configured to:determine respective intensities for one or more of the captured pattern features, wherein the one or more captured pattern features having an intensity that is below the intensity threshold fail to satisfy the criterion (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133… The determination of the motion of the subject may refer to operation 610 described in FIG. 6. In some embodiments, the motion may include a relatively stable motion phase (e.g., the mid and late diastole, the expiratory phase, the eye opening phase) and a relatively unstable motion phase (e.g., the systole, the inspiratory phase, the eye blinking phase). A portion of the scanning data may correspond to the relatively stable motion phase of the motion, while another portion of the scanning data may correspond to the relatively unstable motion phase of the motion- ¶0140… In 704, the processing device 140 (e.g., the scanning module 508 of the processing device 140) may extract the portion of the scanning data corresponding to the stable motion phase of the motion. In some embodiments, the processing device 140 may determine the stable motion phase of the motion and determine a portion of the scanning data corresponding to the stable motion phase. The processing device 140 may then extract the portion of the scanning data. The extracted portion of the scanning data may be barely affected by the motion of the object- ¶0141… It should be understood that according to the reconstruction of the scanning image of the ROI using the scanning data acquired or obtained when the motion of the object is relatively stable, motion artifacts in the reconstructed image may be effectively reduced or removed- ¶0143).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 26, Mark, Henrik and Feng teach all the limitations of claim 22 and Mark further teaches:
wherein: the plurality of cameras comprises a first camera and a second camera; and the one or more processors are configured to determine the correspondence by determining agreement between the first camera and the second camera that the projected features of the projected structured light pattern captured by the first camera and the second camera are located at the 3D points in space(i.e. Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030).
Regarding claim 27, Mark, Henrik and Feng teach all the limitations of claim 22 and Mark further teaches:
wherein:each of the first camera and the second camera comprise a camera sensor that has an array of pixels, for each of which there exists a corresponding camera ray in 3D space originating from the pixel whose direction is towards the intraoral object being captured; andthe one or more processors are configured to determine agreement between the first camera and the second camera by determining, for each projector ray associated with the projected features of the projected structured light pattern captured by the first camera and the second camera, intersections with camera rays of the first camera and intersections with camera rays of the second camera(i.e. Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030).
Regarding claim 28, Mark, Henrik and Feng teach all the limitations of claim 22.
However, Mark does not teach explicitly:
wherein the light source comprises a light emitting diode (LED), wherein the pattern generating optical element comprises a transmission mask or a transparency mask, and wherein the structured light pattern comprises a checkerboard pattern.
In the same field of endeavor, Henrik teaches:
wherein the light source comprises a light emitting diode (LED), wherein the pattern generating optical element comprises a transmission mask or a transparency mask, and wherein the structured light pattern comprises a checkerboard pattern (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment Application/Control Number: 19/238,333 Page 11 Art Unit: 2488 of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130 (a line in a true cross-sectional view, but shown here at an angle for clarity)… FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark with the teachings of Henrik to speed up the acquisition process (Henrik- ¶0082).
Regarding claim 29, Mark teaches:
29. (New) An intraoral scanning system (i.e. FIG. 5 is a dental system in which the 3D photogrammetry technique of this disclosure may be implemented- ¶0014… A intra-oral laser digitizer system provides a three-dimensional visual image of a real-world object such as a dental item through a laser digitization- Abstract, Henley) comprising:
an elongate handheld wand with a probe at a distal end (i.e. The imaging system may include a small image sensor and objective lens mounted directly at the end of the sensor probe to provide a smaller intra-oral probe through elimination of the relay lenses- col 12, line 57-65) at a distal end (i.e. The enclosure may be a hand-held enclosure or have a form factor suitable for being handheld, for enclosing the electrical circuit 112 and for manipulating the intra-oral digitizer 100 in vivo- Col 5, line 62-65, Henley);
(i.e. A pattern typically comprises a plurality of curves, with each curve being substantially parallel to one another- ¶0019), the structured light pattern projector comprising a light source configured to transmit light (i.e. a projecting coupling lens system 205, a laser or other light source system 20- ¶0036, fig. 6);
a plurality of cameras configured to capture features of the structured light pattern projected onto the intraoral object by the structured light pattern projector (i.e. An alternative method may be used when a two camera system is employed. In this case, a dense laser pattern is projected onto the object to be digitized. Two images are obtained from two different positions, simultaneously. In this case, the denser the laser pattern, the better. Application/Control Number: 19/238,333 Page 7 Art Unit: 2488 The laser also has the effect of producing a laser speckle pattern, such that, in addition to the intended illuminated pattern on the object, other blobs of reflected light are visible. In the case of a two camera system where the two images are taken simultaneously, this laser speckle pattern has the added benefit of providing additional feature points on the surface that are now imaged from two cameras- ¶0033-36, figs. 2-4); and
one or more processors configured to:
determine a correspondence between projected features of the structured light pattern generated by the structured light pattern projector and the captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object;use the determined correspondence to determine three-dimensional (3D) points on the intraoral object that are associated with the captured features of the structured light pattern captured by the plurality of cameras viewing the structured light pattern projected onto the intraoral object (i.e. For each position M (where M goes from 1 through N), as part of the 3D computation, one may determine a function FM(x,y,z)[Wingdings font/0xE0](a,b), where (x, y, z) is a real world 3D coordinate, and (a, b) is a pixel coordinate. Thus, for each position, a 3D coordinate may be mapped back to a corresponding pixel location on the image sensor- ¶0026…Matching pixels between different positions (i.e., for different values of M) are then determined. This can be done by examining each pixel in the set SM, and finding corresponding pixels in another set SQ where M≠Q. There may be more than one set with a matching pixel. Well-known photogrammetry operations, such as cross-correlation, may be used to determine matching pixels. To aid in the search process, the system may take advantage of the fact that each pixel in SM also corresponds to a 3D coordinate in CM, and that FM may be applied to that coordinate to obtain an initial guess for a neighborhood in SM for searching. For example, if (a, b) is a pixel in SM, a corresponding 3D coordinate (x, y, z) is related to the pixel. FQ(x, y, z)=(c, d) produces a new pixel for position Q. For all subsequent searches in SQ to determine correspondence, the computations may then be restricted to a neighborhood of (c, d), thus significantly reducing the computational load for determining correspondence- ¶0028… Once the correspondence has been determined for all pixels, for any pixel that is found to correspond to at least one other pixel from another view, the next step is to form a Application/Control Number: 19/238,333 Page 8 Art Unit: 2488 bundle of rays from the origin of the camera to the pixel coordinate, with a ray for each corresponding pixel. A ray may be determined as a vector from the origin of the camera system through the pixel in question, and it may be determined from the camera model. Each bundle of rays is then converted into a 3D coordinate by intersecting the rays. If the rays do not intersect, a point of closest approach is chosen as long as that closest approach is within some maximum tolerance. The set of the 3D coordinates then forms the point cloud for the object- ¶0030); and
generate a digital 3D representation of the intraoral object based on the determined 3D points (i.e. A structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object. The structured light digitizing operation generates a 3D model of the object being scanned, and this model is then used to compute a higher accuracy model using photogrammetry- Abstract… In a representative method, the use of a structured light pattern digitizing method is combined with photogrammetry to determine a 3D model of an object- ¶0007).
However, Mark does not teach explicitly:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element.
In the same field of endeavor, Henrik teaches:
a pattern generating optical element configured to generate the structured light pattern when the light is transmitted from the light source and through the pattern generating optical element (i.e. FIG. 2 schematically shows an example of a handheld part 100 of the intraoral scanner according to an embodiment Application/Control Number: 19/238,333 Page 11 Art Unit: 2488 of this disclosure. It comprises a LED light source 110, a lens 111, a pattern 130 (a line in a true cross-sectional view, but shown here at an angle for clarity)… FIGS. 3 and 4 show how data from the exemplary handheld scanner 100 are processed to yield distance maps. FIG. 3a is a view of one spatial period checkerboard pattern 130 as seen by the image sensor 180 when the image of that spatial period on the scanned surface is in focus- ¶0074).
However, Mark and Henrik do not teach explicitly:
determine, from captured pattern features of the structured light pattern, one or more falsely detected pattern features; wherein one or more captured features of the structured light pattern that fail to satisfy a criterion are removed from consideration for at least one of determining the correspondence or determining the 3D points in space.
In the same field of endeavor, Feng teaches:
wherein the one or more processors are further configured to:
determine, from captured pattern features of the structured light pattern, one or more falsely detected pattern features; wherein one or more captured features of the structured light pattern that fail to satisfy a criterion are removed from consideration for at least one of determining the correspondence or determining the 3D points in space (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Regarding claim 30, Mark, Henrik and Feng teach all the limitations of claim 29.
However, Mark and Henrik do not teach explicitly:
wherein the one or more falsely detected pattern features are detected using feature tracking across a sequence of images captured by the plurality of cameras.
In the same field of endeavor, Feng teaches:
wherein the one or more falsely detected pattern features are detected using feature tracking across a sequence of images captured by the plurality of cameras (i.e. In some embodiments, if it is found that the subject moves during the scan, the imaging system 100 may perform one or more operations to remove or reduce motion artifacts from scanning image(s) generated by the scanning device 110, and thus the quality of the scanning image(s) may be improved. In some embodiments, the monitoring image(s) may include motion related data (or information) of the subject. In some embodiments, the motion related data may be obtained based on a plurality of monitoring images generated at different moments. In some embodiments, the motion artifacts may be removed based on the motion related data- ¶0074… In some embodiments, the processing device 140 may determine, based on the plurality of monitoring images at different moments, the motion of the subject using a motion detection algorithm. Exemplary motion detection algorithms may include a background subtraction algorithm, an optical flow algorithm, an active contour model based tracking algorithm, a continuously adaptive Mean-SHIFT algorithm, a machine learning algorithm (e.g., neural networks), or the like, or any combination thereof. The motion related data may be used to remove or reduce a motion artifact of a scanning image- ¶0133).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Feng to monitor the subject efficiently, and have a good compatibility with the medical device (Feng- ¶0003).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mark S. Quadling et al. [US 20080101688 A1; and incorporated by reference Henley Quadling et al. (US 7184150 B2) and "Multiple View Geometry in Computer Vision" by Richard Hartley and Andrew Zisserman, Second Edition] in view of Henrik Öjelund et al. [US 20210106409 A1] and further in view of Fan Chuanmao et al. [US 20190117075 A1].
Regarding claim 10, Mark and Henrik teach all the limitations of claim 1.
However, Mark and Henrik do not teach explicitly:
further comprising: a broad spectrum light projector, wherein the plurality of cameras are configured to capture the one or more images that comprise color information during projection of broad spectrum light by the broad spectrum light projector.
In the same field of endeavor, Fan teaches:
further comprising: a broad spectrum light projector, wherein the plurality of cameras are configured to capture the one or more images that comprise color information during projection of broad spectrum light by the broad spectrum light projector (i.e. the phrase “broadband light emitter” refers to a light source that emits a continuous spectrum output over a range of wavelengths at any given point of time. Short-coherence or low-coherence, broadband light sources can include, for example, super luminescent diodes, short-pulse lasers, many types of white-light sources, and supercontinuum light sources- ¶0073).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Fan to offer improved acquisition rate and signal-to-noise ratio (Fan- ¶0004)
Regarding claim 11, Mark and Henrik teach all the limitations of claim 10.
However, Mark and Henrik do not teach explicitly:
wherein: the intraoral scanning system is to alternate between projection of the broad spectrum light by the one or more broad spectrum light projectors and the structured light pattern by the structured light pattern projector; and the plurality of cameras are to alternate between capture of the one or more images that comprise the color information and the subset of the plurality of images.
In the same field of endeavor, Fan teaches:
wherein: the intraoral scanning system is to alternate between projection of the broad spectrum light by the one or more broad spectrum light projectors and the structured light pattern by the structured light pattern projector; and the plurality of cameras are to alternate between capture of the one or more images that comprise the color information and the subset of the plurality of images (i.e. Visible light Vis can be of multiple wavelengths in the visible light range. The Vis source can be used for color-coding of the projected structured light pattern, for example. The Vis source can alternately be used for white light image preview or for tooth shade measurement or color or texture characterization- ¶0147… In addition to providing a structured light pattern, the Vis source can alternately provide light of particular wavelengths or broadband light that is scanned over the subject for conventional reflectance imaging, such as for detecting tooth shade, for example, or for obtaining surface contour data by a method that does not employ a light pattern, such as structure-from-motion imaging, for example- ¶0147).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Fan to offer improved acquisition rate and signal-to-noise ratio (Fan- ¶0004)
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mark S. Quadling et al. [US 20080101688 A1; and incorporated by reference Henley Quadling et al. (US 7184150 B2) and "Multiple View Geometry in Computer Vision" by Richard Hartley and Andrew Zisserman, Second Edition] in view of Henrik Öjelund et al. [US 20210106409 A1] and further in view of Benny Pesach et al. [US 20150348320 A1].
Regarding claim 18, Mark and Henrik teach all the limitations of claim 1.
However, Mark and Henrik do not teach explicitly:
wherein the plurality of cameras comprises:
a first camera disposed within the probe, the first camera configured to capture the features of the structured light pattern; a second camera disposed within the probe, the second camera configured to capture the features of the structured light pattern; a third camera disposed within the probe and positioned on a first side of a longitudinal axis of the probe, the third camera configured to capture the features of the structured light pattern; and a fourth camera disposed within the probe and positioned on a second side of the longitudinal axis, the fourth camera configured to capture the features of the structured light pattern.
In the same field of endeavor, Benny teaches:
wherein the plurality of cameras comprises:
a first camera disposed within the probe, the first camera configured to capture the features of the structured light pattern; a second camera disposed within the probe, the second camera configured to capture the features of the structured light pattern; a third camera disposed within the probe and positioned on a first side of a longitudinal axis of the probe, the third camera configured to capture the features of the structured light pattern; and a fourth camera disposed within the probe and positioned on a second side of the longitudinal axis, the fourth camera configured to capture the features of the structured light pattern (i.e. In some embodiments, multiple cameras, (e.g., four cameras) collect images of adjacent teeth. For example, in FIG. 3B, adjacent tooth 344, is seen by cameras 332 and 342. In some embodiments, images of adjacent teeth are used to obtain measurement or models which include adjacent teeth. In some embodiments, measurement or models which include adjacent teeth are used to determine the boundaries of a crown, bridge or other prosthetic. In some embodiments, images of adjacent teeth are used in registration of collected images with a tooth model (e.g., tooth model from intraoral scanner, scanning standard impression, CT)- ¶0183).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Mark and Henrik with the teachings of Benny since increasing the number of SGMP cameras means that if a camera's view is obstructed, another camera or cameras may have an unobstructed view (Benny- ¶0186).
Conclusion
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
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