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 Objections
Claims 7 and 19 are objected to because of the following informalities:
Claims 7 and 19 recites “a three-dimensional coordinate value corresponding to the each two- dimensional” should be “a three-dimensional coordinate value corresponding to each two- dimensional”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 13, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al. (US 20200297187 A1) in view of Somasundaram (US 20180333231 A1).
Regarding claim 1, Wakazome et al. teaches a method for processing a scan image of a three-dimensional scanner, the method being performed by an electronic device comprising at least one processor and at least one memory which stores instructions to be executed by the at least one processor (see para [0006]; “a three-dimensional scanner that generates three-dimensional data of dentition including a plurality of teeth in an oral cavity, an image processing apparatus, and a display. The image processing apparatus includes an acquisition unit that acquires the three-dimensional data from the three-dimensional scanner”, see also para [0008]; “a computer readable storage medium stores a program that causes a computer to perform acquiring three-dimensional data of dentition”), the method comprising: acquiring at least one two-dimensional scan image by scanning of the three-dimensional scanner (see para [0117]; “first image generator 5041 extracts first image data 3011 from three-dimensional data transmitted from acquisition unit 502. [0118] Second image generator 5042 generates second image data 3012 by extracting second image data 3012 of second image 302 showing dentition 200 in second point-of-view direction D2 from three-dimensional data transmitted from acquisition unit 502”) and generating a three-dimensional scan data set regarding a subject's oral cavity based on the acquired at least one two-dimensional scan image (see para [0114]; “generates multi-image data of all dentitions in the oral cavity of which images were picked up by three-dimensional scanner 80”), the three-dimensional scan data set comprising multiple three-dimensional coordinate values (see para [0035]; “Three-dimensional data includes coordinate information and color information associated with the coordinate information. The coordinate information includes, for example, an X-axis coordinate, a Y-axis coordinate, and a Z-axis coordinate”); determining multiple reference coordinate values based on the three-dimensional scan data set (see para [0169]; “In connection with positional relation in representation on display 50, adjuster 5050 adjusts first image data and second image data such that an X coordinate position or a Y coordinate position in display area 50A is identical. For example, when three-dimensional scanner 80 moves substantially in the Y-axis direction as shown in FIG. 8, adjuster 5050 makes adjustment such that Y coordinates of corresponding portions in first image 301, second image 302, and third image 303 are identical as shown in FIG. 3. Display 50 thus shows first image 301, second image 302, and third image 303 such that Y coordinates of corresponding portions in first image 301, second image 302, and third image 303 are identical” Note; the adjuster 5050 uses the reference coordinate values calculated from the scan data to perform the adjustment, ensuring corresponding portions of different images are aligned in the display 50A). However, Wakazome et al. does not teach generating first plane data corresponding to a virtual occlusal plane, generating second plane data corresponding to an occlusal plane of the subject's oral cavity based on the multiple reference coordinate values; and aligning the three-dimensional scan data set on the virtual occlusal plane by matching the first plane data with the second plane data.
In the same field of endeavor, Somasundaram teaches generating first plane data corresponding to a virtual occlusal plane (see para [0018]; “the representative plane (e.g., occlusal plane) is horizontal (step 28)”, see also para [0027]; “Representative planes can be estimated using various techniques”, and claim 10; “each aligned with respective coordinate systems”); generating second plane data corresponding to an occlusal plane of the subject's oral cavity based on the multiple reference coordinate values (see para [0002]; “First and second representative planes are estimated for the mandible and maxilla”, see also Abstract; “The first and second digital 3D models are also transformed such that the mandible and the maxilla are each aligned with the same coordinate system” and see para [0018]; “the representative plane (e.g., occlusal plane) is horizontal (step 28)); and aligning the three-dimensional scan data set on the virtual occlusal plane by matching the first plane data with the second plane data (see para [0002]; “The first and second digital 3D models are transformed such that the first and second representative planes are each aligned with their respective coordinate systems”, see also para [0019]; “bringing the two arches together into at least approximate bite alignment”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. in order to automatically detect and adjust those orientations of digital 3D dental arch pairs (see para [0018]).
Regarding claim 13, the scope of claim 13 is fully encompassed by the scope of claim 1, accordingly, the rejection of claim 1 is fully applicable here.
Regarding claim 25, the scope of claim 25 is fully encompassed by the scope of claim 1, accordingly, the rejection of claim 1 is fully applicable here
Claim 2, 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al. in view of Somasundaram et al. as applied in claim 1 above and further in view of Zhu et al. (CN 107239649 B).
Regarding claim 2, the rejection of claim 1 is fully incorporate herein. The combination of Wakazome et al. and Somasundaram et al. as a whole does not teach wherein the first plane data comprises a first center point, a first anterior tooth point, and a first normal vector, and wherein the second plane data comprises a second center point, a second anterior tooth point, and a second normal vector.
In the same field of endeavor, Zhu et al. teaches wherein the first plane data comprises a first center point, a first anterior tooth point, and a first normal vector, and wherein the second plane data comprises a second center point, a second anterior tooth point, and a second normal vector (see page 14, last para; “The dental coordinates in the present invention are divided into upper dental coordinates (fig. 12a) and lower dental coordinates (fig. 12 b) The center of the dental jaw coordinate is defaulted as a center of mass ….The positive Z-axis (greater mother pointing up) of the upper jaw coordinate axis indicates that the coordinate center points to the upper jaw, that is: the coordinate center points to the upper root growth direction…. The positive direction of the Z axis of the coordinate axis of the lower jaw (the big mother points downwards) represents that the coordinate center points to the lower jaw, namely the coordinate center points to the growth direction of the lower tooth root; x-axis forward (index finger) indicates coordinate center pointing mesial”, see also Fig. 13; “the coordinate center of the anterior tooth of the tooth root; b, generating a coordinate center schematic diagram of a posterior tooth of the tooth root”, page 10 6th para; “wherein the anterior tooth arrangement characteristics are mainly embodied by incisor edge angles of upper anterior teeth (13, 12, 11, 21, 22 and 23) and lower anterior teeth (43, 42, 41, 31, 32 and 33), the incisor edge angles are characterized by comprising a combined plane” and page 7, last para; “firstly, estimating the normal vector of each vertex P in a triangular patch” Note; “First plane data / Second plane data" corresponds to the upper dental coordinates / lower dental coordinate, center of mass corresponds to center point, "Normal vector" determines the direction of the Z-axis). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a method of oral cavity parametrization measures of Zhgu et al. in order to increase the efficiency and precision of specialized department for stomatology inspection (see page 1, last para).
Regarding claim 14, the rejection of claim 13 is fully incorporate herein.
Zhu et al. in the combination further teaches wherein the first plane data comprises a first center point, a first anterior tooth point, and a first normal vector, and wherein the second plane data comprises a second center point, a second anterior tooth point, and a second normal vector (see page 14, last para; “The dental coordinates in the present invention are divided into upper dental coordinates (fig. 12a) and lower dental coordinates (fig. 12 b) The center of the dental jaw coordinate is defaulted as a center of mass ….The positive Z-axis (greater mother pointing up) of the upper jaw coordinate axis indicates that the coordinate center points to the upper jaw, that is: the coordinate center points to the upper root growth direction…. The positive direction of the Z axis of the coordinate axis of the lower jaw (the big mother points downwards) represents that the coordinate center points to the lower jaw, namely the coordinate center points to the growth direction of the lower tooth root; x-axis forward (index finger) indicates coordinate center pointing mesial”, see also Fig. 13; “the coordinate center of the anterior tooth of the tooth root; b, generating a coordinate center schematic diagram of a posterior tooth of the tooth root”, page 10 6th para; “wherein the anterior tooth arrangement characteristics are mainly embodied by incisor edge angles of upper anterior teeth (13, 12, 11, 21, 22 and 23) and lower anterior teeth (43, 42, 41, 31, 32 and 33), the incisor edge angles are characterized by comprising a combined plane” and page 7, last para; “firstly, estimating the normal vector of each vertex P in a triangular patch”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a method of oral cavity parametrization measures of Zhgu et al. in order to increase the efficiency and precision of specialized department for stomatology inspection (see page 1, last para).
Claims 3-6 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al. in view of Somasundaram et al. as applied in claim 1 above and further in view of Azernikov et al. (US 20220215531 A1).
Regarding claim 3, the rejection of claim 1 is fully incorporate herein.
Wakazome et al. in the combination further teaches wherein determining the multiple reference coordinate values is performed using a trained artificial neural network model (see para [0213]; “by using artificial intelligence (AI) of the image processing apparatus”, see also para [0215]; “Estimation model 114 includes a neural network and a parameter of the neural network”, and para [0218]; “Estimation model 114 is thus trained based on three-dimensional data including characteristics of a tooth and a result of identification (for example, a score) of a target tooth using the three-dimensional data”, and see para [0035]; “Three-dimensional data includes coordinate information and color information associated with the coordinate information”), and a tooth number corresponding to each of the at least one two-dimensional training image (see para [0073]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
However, the combination of Wakazome et al. and Somasundaram as a whole does not teach wherein the artificial neural network model is trained based on a training data set comprising: curvature information of each of at least one two-dimensional training image.
In the same field of endeavor, Azernikov et al. teaches and wherein the artificial neural network model is trained based on a training data set comprising: curvature information of each of at least one two-dimensional training image (see para [0097]; “the 2D depth map trained CNN can be a 2D depth map trained YOLO network as described previously. The trained 2D depth map YOLO network can receive a 2D depth map and can provide a digital tooth bounding region for each digital tooth in at least a portion of the 2D depth map…. the 2D depth map trained neural network is a 2D depth map trained semantic segmentation network”, see also para [0141]; “curvature-based segmentation can include curvature determination of digital surface regions in the digital model”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a system of digitally segmenting teeth in a digital model of Azernikoy et al. in order to provide a segmented 3D digital model (see para [0097]).
Regarding claim 4, the rejection of claim 3 is fully incorporate herein.
Azernikov et al. in the combination further teach wherein the training data set further comprises at least one selected from the group of: size information of each of the at least one two-dimensional training image; and shape information of each of the at least one two-dimensional training image (see para [0096]; “the computer-implemented method can use the trained neural network to roughly define a digital tooth bounding region around each digital tooth …Although a bounding box 802 is shown, each digital tooth bounding region can be of any suitable shape and/or size to bound the particular digital tooth in some embodiments”).
Regarding claim 5, the rejection of claim 1 is fully incorporate herein.
Wakazome et al. in the combination further teaches and identifying a tooth number of each of the at least one two-dimensional scan image based on the input curvature information (see para [0054]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
Azernikov et al. in the combination further teach wherein determining the multiple reference coordinate values comprises: inputting curvature information of each of the at least one two-dimensional scan image into a trained artificial neural network model (see para [0141]; “the computer-implemented method can receive a digital model and determine curvatures of digital surface regions”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a system of digitally segmenting teeth in a digital model of Azernikoy et al. in order to provide a segmented 3D digital model (see para [0141]).
Regarding claim 6, the rejection of claim 5 is fully incorporate herein.
Wakazome et al. in the combination further teaches and wherein identifying the tooth number is performed additionally based on the input of at least one selected from the group of the size information and the shape information (see para [0054]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
Azernikov et al. in the combination further teaches wherein determining the multiple reference coordinate values further comprises inputting at least one selected from the group of size information and shape information of the at least one two-dimensional scan image into the trained artificial neural network model (see para [0096]; “the computer-implemented method can use the trained neural network to roughly define a digital tooth bounding region around each digital tooth …Although a bounding box 802 is shown, each digital tooth bounding region can be of any suitable shape and/or size to bound the particular digital tooth in some embodiments”).
Regarding claim 15, the rejection of claim 13 is fully incorporate herein.
Wakazome et al. in the combination further teaches wherein determining the multiple reference coordinate values is performed using a trained artificial neural network model (see para [0213]; “by using artificial intelligence (AI) of the image processing apparatus”, see also para [0215]; “Estimation model 114 includes a neural network and a parameter of the neural network”, and para [0218]; “Estimation model 114 is thus trained based on three-dimensional data including characteristics of a tooth and a result of identification (for example, a score) of a target tooth using the three-dimensional data”, and see para [0035]; “Three-dimensional data includes coordinate information and color information associated with the coordinate information”), and a tooth number corresponding to each of the at least one two-dimensional training image (see para [0073]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
In the same field of endeavor, Azernikov et al. in the combination further teaches and wherein the artificial neural network model is trained based on a training data set comprising: curvature information of each of at least one two-dimensional training image (see para [0097]; “the 2D depth map trained CNN can be a 2D depth map trained YOLO network as described previously. The trained 2D depth map YOLO network can receive a 2D depth map and can provide a digital tooth bounding region for each digital tooth in at least a portion of the 2D depth map…. the 2D depth map trained neural network is a 2D depth map trained semantic segmentation network”, see also para [0141]; “curvature-based segmentation can include curvature determination of digital surface regions in the digital model”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a system of digitally segmenting teeth in a digital model of Azernikoy et al. in order to provide a segmented 3D digital model (see para [0097]).
Regarding claim 16, the rejection of claim 13 is fully incorporate herein.
Azernikov et al. in the combination further teaches wherein the training data set further comprises at least one selected from the group of: size information of each of the at least one two-dimensional training image; and shape information of each of the at least one two-dimensional training image (see para [0096]; “the computer-implemented method can use the trained neural network to roughly define a digital tooth bounding region around each digital tooth …Although a bounding box 802 is shown, each digital tooth bounding region can be of any suitable shape and/or size to bound the particular digital tooth in some embodiments”).
Regarding claim 17, the rejection of claim 13 is fully incorporate herein.
Wakazome et al. in the combination further teaches and identify a tooth number of each of the at least one two-dimensional scan image based on the input curvature information (see para [0073]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
Azernikov et al. in the combination further teaches wherein the at least one processor is configured to: input curvature information of each of the at least one two-dimensional scan image into a trained artificial neural network model (see para [0097]; “the 2D depth map trained CNN can be a 2D depth map trained YOLO network as described previously. The trained 2D depth map YOLO network can receive a 2D depth map and can provide a digital tooth bounding region for each digital tooth in at least a portion of the 2D depth map…. the 2D depth map trained neural network is a 2D depth map trained semantic segmentation network”, see also para [0141]; “curvature-based segmentation can include curvature determination of digital surface regions in the digital model”).8, the rejection of claim 17 is fully incorporate herein.
Wakazome et al. in the combination further teaches wherein the at least one processor is configured to: input at least one selected from the group of size information and shape information of the at least one two-dimensional scan image into the trained artificial neural network model (see para [0054]; “information representing target tooth 360 (for example, identification information (identification number) of a tooth) can be input”).
Azernikov et al. in the combination further teaches and identify the tooth number of each of the at least one two-dimensional scan image based on the input curvature information and additionally based on the input of at least one selected from the group of the size information and the shape information (see para [0096]; “the computer-implemented method can use the trained neural network to roughly define a digital tooth bounding region around each digital tooth …Although a bounding box 802 is shown, each digital tooth bounding region can be of any suitable shape and/or size to bound the particular digital tooth in some embodiments”).
Claims 7-8, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al., and Somasundaram et al. in view of Azernikov et al. as applied in claim 1 and 5 above and further in view of Peng et al. (CN112515787B).
Regarding claim 7, the rejection of claim 5 is fully incorporate herein. The combination of Wakazome et al., Somasundaram and Azernikov et al. as a whole does not teach wherein determining the multiple reference coordinate values further comprises determining, based on the identified tooth number of each two- dimensional scan image, a tooth number of a three-dimensional coordinate value corresponding to the each two-dimensional scan image and included in the three-dimensional scan data set.
In the same field of endeavor, Peng et al. teaches wherein determining the multiple reference coordinate values further comprises determining, based on the identified tooth number of each two- dimensional scan image (see Abstract; “a dental plane to obtain a two-dimensional mapping image, and identifying the position of each dental crown in the two-dimensional mapping image through a two-dimensional image processing algorithm”, see also page 6, 4th para; “identifying the type of each dental crown based on the position of each dental crown in the dental arch curve, and obtaining the unique number of the dental crown in the stomatology based on the position relation between the type of the dental crown and the dental arch curve”), a tooth number of a three-dimensional coordinate value corresponding to the each two-dimensional scan image and included in the three-dimensional scan data set (see page 6, 6th para; “identifying the corresponding seed area by using the number ……back projecting the two-dimensional pixel points to a three-dimensional point cloud of the three-dimensional dental jaw scanning model to obtain a mapping point of each dental crown in the three-dimensional point cloud”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a system of digitally segmenting teeth in a digital model of Azernikoy et al. and further in view of Three-dimensional dental data analysis method of Peng et al. in order the research of three-dimensional analysis to be accurate and practical. (see Abstract).
Regarding claim 8, the rejection of claim 7 is fully incorporate herein.
Peng et al. in the combination further teaches wherein determining the multiple reference coordinate values further comprises determining a representative coordinate value of a corresponding tooth based on multiple three-dimensional coordinate values determined to have the same tooth number (see page 4, 1st para; “fitting the mapping points of each dental crown in the three-dimensional point cloud to obtain a dental arch curve……. a plurality of two-dimensional pixel points”, see also page 6, 4th para; “obtaining the unique number of the dental crown…… identifying the corresponding seed area by using the number”).
Regarding claim 19, the rejection of claim 17 is fully incorporate herein.
Peng et al. et al. in the combination further teaches wherein the at least one processor is configured to determine, based on the identified tooth number of each two-dimensional scan image (see Abstract; “a dental plane to obtain a two-dimensional mapping image, and identifying the position of each dental crown in the two-dimensional mapping image through a two-dimensional image processing algorithm”, see also page 6, 4th para; “identifying the type of each dental crown based on the position of each dental crown in the dental arch curve, and obtaining the unique number of the dental crown in the stomatology based on the position relation between the type of the dental crown and the dental arch curve”), a tooth number of a three-dimensional coordinate value corresponding to the each two-dimensional scan image and included in the three-dimensional scan data set(see page 6, 6th para; “identifying the corresponding seed area by using the number ……back projecting the two-dimensional pixel points to a three-dimensional point cloud of the three-dimensional dental jaw scanning model to obtain a mapping point of each dental crown in the three-dimensional point cloud”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and a system of digitally segmenting teeth in a digital model of Azernikoy et al. and further in view of Three-dimensional dental data analysis method of Peng et al. in order the research of three-dimensional analysis to be accurate and practical. (see Abstract).
Claim 9, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al. and Somasundaram et al. as applied in claim 1 in view of Masoud et al. (US 20150265374 A1) and further in view of Inglese et al. (US 20170258420 A1).
Regarding claim 9, the rejection of claim 1 is fully incorporate herein. The combination of Wakazome et al., Somasundaram and Azernikov et al. as a whole does not teach wherein generating the second plane data comprises: determining whether the multiple reference coordinate values comprise a first coordinate value included in a left molar area, a second coordinate value included in a right molar area, and a third coordinate value included in an anterior tooth area; storing the third coordinate value as an anterior tooth point of the second plane data; calculating, as a center point of the second plane data, a center point of the first coordinate value, the second coordinate value, and the third coordinate value; and calculating, as a normal vector of the second plane data, a vector perpendicular to a plane comprising the first coordinate value, the second coordinate value, and the third coordinate value.
In the same field of endeavor, Masoud et al. teaches wherein generating the second plane data comprises: determining whether the multiple reference coordinate values comprise a first coordinate value included in a left molar area, a second coordinate value included in a right molar area, and a third coordinate value included in an anterior tooth area (see table 4; “MxO plane (maxillary Plane connecting UR6MB,UL6MB, and occlusal plane) midway between UL4O and UR4O (best fit)”, para [0161]; “UR first first molar: Mesiopalatal cusp UR6MP tip of the mesiopalatal cusp of the tooth UL first molar”, Note; defines a specific, mathematically calculated plane that passes through the molar points and averages the position of the anterior teeth (e.g., premolars/canines) to fit the plane to the specific morphology of that patient's dental); storing the third coordinate value as an anterior tooth point of the second plane data (see table 4; “Plane connecting UR6MB,UL6MB, and occlusal plane) midway between UL4O and UR4O”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and systems for evaluating the facial and dental features of a patient for orthodontic diagnosis and treatment of Masoud et al. in order to assist in developing a treatment plan and evaluating progress during treatment (see table 4).
However, the combination of Wakazome et al., Somasundaram et al. and Masoud et al. as a whole does not teach calculating, as a center point of the second plane data, a center point of the first coordinate value, the second coordinate value, and the third coordinate value; and calculating, as a normal vector of the second plane data, a vector perpendicular to a plane comprising the first coordinate value, the second coordinate value, and the third coordinate value.
In the same field of endeavor Inglese et al. teaches calculating, as a center point of the second plane data, a center point of the first coordinate value, the second coordinate value, and the third coordinate value (see para [[0106]; “The jaw curve is constructed to intersect with the mass center of each tooth in the respective jaw and to lie in the corresponding jaw plane. The mass center of the tooth can be calculated, in turn, using the 3-D position list and the code value list for the segmented teeth”, see also para [0130]; “An exemplary origin for system 1302 is the mass center of the tooth that is associated with axis 1006”); and calculating, as a normal vector of the second plane data, a vector perpendicular to a plane comprising the first coordinate value, the second coordinate value, and the third coordinate value (see para 0105]; “A 3-D plane can be formed using two of the eigenvectors, or using one of the eigenvectors as the plane normal”, see also para [0109]; “The normal of plane 908 is the average of the normal of plane 702 and normal of plane 704”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and systems for evaluating the facial and dental features of a patient for orthodontic diagnosis and treatment of Masoud et al and further in view of a method for 3-D cephalometric analysis of a patient of Inglese et al. in order to track patient progress at different stages of ongoing treatment (see [0106]).
Regarding claim 11, the rejection of claim 1 is fully incorporate herein.
Masoud et al. in the combination further teach wherein generating the second plane data comprises: determining whether the multiple reference coordinate values comprise a first coordinate value included in a left molar area, a second coordinate value included in a right molar area, a third coordinate value included in a left area of the subject's oral cavity and different from the first coordinate value, and a fourth coordinate value included in a right area of the subject's oral cavity and different from the second coordinate value (see Table 4; “UR6MB,UL6MB, and occlusal plane) midway between UL4O and UR4O”, Note; left molar area UR6MB, right molar area UL6MB, left non-molar area UL4O, and right non-molar area UR4O); calculating a first midpoint, which is a midpoint of the third coordinate value and the fourth coordinate value (see Table 4; “midway between UL4O and UR4O”).
Inglese et al. in the combination further teaches calculating, as a center point of the second plane data, a center point of the first coordinate value, the second coordinate value, and the first midpoint (see para [0106]; “The jaw curve is constructed to intersect with the mass center of each tooth in the respective jaw and to lie in the corresponding jaw plane. The mass center of the tooth can be calculated, in turn, using the 3-D position list and the code value list for the segmented teeth”); and calculating, as a normal vector of the second plane data, a vector perpendicular to a plane comprising the first coordinate value, the second coordinate value, and the first midpoint (see para [0105]; “using one of the eigenvectors as the plane normal”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and systems for evaluating the facial and dental features of a patient for orthodontic diagnosis and treatment of Masoud et al and further in view of a method for 3-D cephalometric analysis of a patient of Inglese et al. in order to track patient progress at different stages of ongoing treatment (see [0106]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al. in view of Somasundaram et al. as applied in claim 1 above and further in view of Li et al. NPL “New approach to establish an object reference frame for dental arch in computer-aided surgical simulation”.
Regarding claim 10, the rejection of claim 1 is fully incorporate herein.
Somasundaram et al. in the combination further teach wherein aligning the three-dimensional scan data set on the virtual occlusal plane comprises: matching a first center point included in the first plane data with a second center point included in the second plane data (see para [0015]; “the arches are centered about the origin”, see also para [0044]; “Then, the maxilla is shifted in the X-Z plane such that its centroid is aligned with the centroid of the mandible”, see also para [0002][; “The first and second digital 3D models are transformed such that the first and second representative planes are each aligned with their respective coordinate systems”); matching a first normal vector included in the first plane data with a second normal vector included in the second plane data (see para [0002]; “First and second representative planes are estimated for the mandible and maxilla. The first and second digital 3D models are transformed such that the first and second representative planes are each aligned with their respective coordinate systems”, see also para [0018]; “If the scans are already bite-aligned, then method 22 includes transforming (e.g., rotating) both arches such that the representative plane (e.g., occlusal plane) is horizontal”).
However, the combination of Wakazome et al. and Somasundaram et al. as a whole does not teach and matching a first straight line passing through the first center point and a first anterior tooth point included in the first plane data with a second straight line passing through the second center point and a second anterior tooth point included in the second plane data.
In the same field of endeavor, Li et al. teaches and matching a first straight line passing through the first center point and a first anterior tooth point included in the first plane data with a second straight line passing through the second center point and a second anterior tooth point included in the second plane data (see page1194, 1st col.3rd para; “This method utilizes three points: the midpoint between the two central incisal embrasures (U0), and the right and left mesiobuccal cusps of the first molars (U6)1–3. In this method, the origin of the object reference frame is U0”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. in view of a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. and new approach to establish an object reference frame for dental arch in computer-aided surgical simulation of Li et al. in order to establish an optimal object reference frame for symmetrical alignment of the dental arch during computer-aided surgical simulation (CASS) (see page1194, 1st col.3rd para).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wakazome et al., Somasundaram et al., Masoud et al. and Inglese et al. as applied in claims 1, and 11 in view of Li et al. and further in view of Raslambekov et al. (US 11116606 B1).
Regarding claim 12, the rejection of claim 11 is fully incorporate herein.
Somasundaram et al. in the combination further teach wherein aligning the three-dimensional scan data set on the virtual occlusal plane comprises: matching a first center point included in the first plane data with a second center point included in the second plane data (see para [0015]; “the arches are centered about the origin”, see also para [0044]; “the maxilla is shifted in the X-Z plane such that its centroid is aligned with the centroid of the mandible”); matching a first normal vector included in the first plane data with a second normal vector included in the second plane data (see para [0002]; “First and second representative planes are estimated for the mandible and maxilla. The first and second digital 3D models are transformed such that the first and second representative planes are each aligned with their respective coordinate systems”); aligning the three-dimensional scan data set on the second plane data (see para [0015]; “the technique estimates 3D transformations, including rigid-body rotations and translations, that move the arches into positions in 3D space such that the following can occur: the arches are bite-aligned with one another so that the teeth are in at least approximate occlusion; the bite plane is horizontal, parallel to the X-Z plane; the arches are oriented in a standard direction, facing along the Z-axis; and the arches are centered about the origin”). Masoud et al. in the combination further teach the second-line target point by defining the maxillary occlusal plane as (see Table 4; “Plane connecting UR6MB,UL6MB, and occlusal plane) midway between UL4O and UR4O (best fit)”. Somasundaram et al. in the combination then teach the directional alignment step (see para [0019]; “finding the in-plane rotation about the Y-axis that points the arches along the Z-axis”). However, the combination of Wakazome et al., Somasundaram et al., Masoud et al., Inglese et al. as a whole does not teach matching a first straight line passing through the first center point and a first anterior tooth point included in the first plane data with a second straight line passing through the second center point and the first midpoint, obtaining position information based on the first anterior tooth point and a farthest point from the second center point toward the first midpoint among multiple three-dimensional coordinate values included in the three-dimensional scan data set; and aligning the three-dimensional scan data set on the virtual occlusal plane by correcting, based on the position information, the three-dimensional scan data set aligned on the second plane data.
In the same field of endeavor, Li et al. teaches matching a first straight line passing through the first center point and a first anterior tooth point included in the first plane data with a second straight line passing through the second center point and the first midpoint (see page 1194, col. 1, last para; “the midpoint between the two central incisal embrasures (U0…. the origin of the object reference frame is U0)”, see also page 1196, Fig. 3; “The origin O0 is located in the middle of the dental arch”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the general use of an image processing apparatus processes an image of dentition of teeth in an oral cavity of Wakazome et al. and a method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla of Somasundaram et al. in view of systems for evaluating the facial and dental features of a patient for orthodontic diagnosis and treatment of Masoud et al and a method for 3-D cephalometric analysis of a patient of Inglese et al. and further in view of new approach to establish an object reference frame for dental arch in computer-aided surgical simulation of Li et al. in order to establish an optimal object reference frame for symmetrical alignment of the dental arch during computer-aided surgical simulation (CASS) (see page1194, 1st col.3rd para).
However, the combination of Wakazome et al., Somasundaram et al., Masoud et al., Inglese et al. and Li et al. as a whole does not teach obtaining position information based on the first anterior tooth point and a farthest point from the second center point toward the first midpoint among multiple three-dimensional coordinate values included in the three-dimensional scan data set; and aligning the three-dimensional scan data set on the virtual occlusal plane by correcting, based on the position information, the three-dimensional scan data set aligned on the second plane data.
In the same field of endeavor, Raslambekov et al. teaches obtaining position information based on the first anterior tooth point and a farthest point from the second center point toward the first midpoint among multiple three-dimensional coordinate values included in the three-dimensional scan data set (see Abstract; “an average point of the tooth contour of the given tooth”, col. 2,lines 51-56; “determining a left line segment extending through the tooth contour center of two rear-most, left teeth, the left line segment being projected in the jaw plane, and locating a left anchor point on the left line segment at a predetermined distance from a rear-most one of the two rear-most, left teeth”); and aligning the three-dimensional scan data set on the virtual occlusal plane by correcting, based on the position information, the three-dimensional scan data set aligned on the second plane data (see col. 4, lines 25-30; “a normalized position value of a tooth contour center for each tooth along a jaw curve; and determining the displacement of the tooth mesh of the primary tooth based at least in part on the normalized position value of the tooth contour center”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date to the claimed invention to modify the teaching of Wakazome et al., Somasundaram et al. and Masoud et al in view of a method for 3-D cephalometric analysis of a patient of Inglese et al. and new approach to establish an object reference frame for dental arch in computer-aided surgical simulation of Li et al. and further in view of a method for determining a jaw curve for orthodontic treatment planning for a patient of Raslambekov et al. in order to prevent damages to the subject's teeth and minimize the overall duration of the orthodontic treatment of the subject (see Abstract).
Conclusion
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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
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