DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Status
Claims 1-17 were pending for examination in the application filed April 21, 2023. Claims 1-9, 12-13 and 17 are amended, no claims cancelled, and claims 18-20 are added as of the remarks received September 2, 2025. Accordingly, claims 1-20 are currently pending in the application for examination.
Claim Rejections - 35 USC § 112
Claim 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 recites the limitation “the second curvature threshold,” there is insufficient antecedent basis for this limitation in the claim. The Examiner will be interpreting the second curvature threshold as separating the tooth mass into the plurality of tooth pieces, as claimed in claim 18, from which claim 19 appears to intend to depend.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Independent claim 1 recites:
A method for processing an intraoral image in an intraoral image processing apparatus, the method comprising:
receiving an intraoral image generated by scanning teeth from a server or a medical device;
aligning a teeth model template including a plurality of template teeth to teeth in the intraoral image;
obtaining a plurality of tooth pieces by separating the intraoral image into a tooth mass and gingiva according to a curvature distribution, and separating the tooth mass into a plurality of tooth pieces according to the curvature distribution, wherein each tooth is separated into two or more tooth pieces according to the curvature distribution and
individualizing the teeth of the intraoral image by identifying each tooth piece corresponding to each template tooth and collecting one or more tooth pieces corresponding to each of the aligned template teeth.
Under Step 2A prong 1, the italicized portions are interpreted as abstract ideas, specifically:
obtaining an intraoral image generated by scanning teeth; (Mental process)
aligning a teeth model template including a plurality of template teeth to teeth in the intraoral image; (Mental process)
obtaining a plurality of tooth pieces by separating the teeth of the intraoral image; and (Mental process)
individualizing the teeth of the intraoral image by collecting one or more tooth pieces corresponding to each of the aligned template teeth. (Mental process)
A human being could perform these mental processes by examining and observing content, comparing images, and dividing images.
Under Step 2A prong 2, the claim does not recite any additional elements which integrate the judicial exception into a practical application.
Dependent claims 2-8, 18-20 add limitations that fail to meaningfully tie the abstract idea into a practical application or add significantly more.
Dependent claim 2 recites the following additional limitations: wherein the individualizing of the teeth of the intraoral image comprises: obtaining a mapping relationship between each tooth piece of the intraoral image and each of the template teeth by identifying the tooth piece corresponding to each aligned template tooth (Mental process); and collecting the one or more tooth pieces mapped to each template tooth by using the mapping relationship (Mental process).
Dependent claim 3 recites the following additional limitations: wherein the aligning of the teeth model template including the plurality of template teeth to the teeth in the intraoral image comprises positioning the plurality of template teeth of the teeth model template to correspond to the teeth in the intraoral image (Mental process).
Dependent claim 4 recites the following additional limitations: wherein the obtaining of the plurality of tooth pieces by separating the teeth of the intraoral image comprises separating each tooth into one or more tooth pieces according to a curvature distribution comprising principal curvatures of a point on a surface of a 3D model representing a criterion for indicating degrees in which the surface is bent from the point in different directions (Mathematical calculation).
Dependent claim 5 recites the following additional limitations: wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using an orientation condition of each template tooth and each tooth piece wherein the orientation condition represents a condition for identifying the tooth piece corresponding to the template tooth by using a normal vector of the template tooth and a normal vector of the tooth piece (Mathematical calculation).
Dependent claim 6 recites the following additional limitations: wherein the identifying of each tooth piece corresponding to each template tooth by using the orientation condition comprises: identifying a crossing point at which a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece (Mathematical calculation); and determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value (Mathematical calculation).
Dependent claim 7 recites the following additional limitations: wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using a distance condition of each template tooth and each tooth piece (Mathematical calculation), and the identifying of each tooth piece corresponding to each template tooth comprises: identifying a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece (Mathematical calculation); and determining that the tooth piece corresponds to the template tooth based on a distance from the vertex to the crossing point being less than a threshold value (Mathematical calculation).
Dependent claim 8 recites the following additional limitations: wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using a distance condition and an orientation condition of each template tooth and each tooth piece (Mathematical calculation), and the identifying of each tooth piece corresponding to each template tooth by using the distance condition and the orientation condition comprises: identifying, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex (Mathematical calculation); and determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value (Mathematical calculation).
Dependent claim 18 recites the following additional limitations: wherein the obtaining of the plurality of tooth pieces comprises: separating the tooth mass and the gingiva from each other in the intraoral image by using a first curvature threshold used for separating a boundary between the tooth mass and the gingiva (Mental Process); and separating the tooth mass into the plurality of tooth pieces by using a second curvature threshold used for separating a boundary of each tooth piece in the tooth mass (Mental Process).
Dependent claim 19 recites the following additional limitations: wherein the second curvature threshold is the same as or different from the first curvature threshold (Mathematical calculation/Mental Process).
Dependent claim 20 recites the following additional limitations: wherein each tooth is separated into the one or more tooth pieces by cutting and segmenting, into the pieces, a portion having a value of the distribution min (k1, k2) which is less than a threshold (Mathematical calculation), wherein the value of the distribution min (kl, k2) indicates a less value of the principal curvatures kl and k2 assessed on each vertex of a mesh structure forming the intraoral image (Mathematical calculation).
Independent claim 9 recites the following additional limitations compared to independent claim 1:
An intraoral image processing device comprising a communications interface, a processor and a memory,
Under Step 2A prong 2, the claim recites additional limitations in the underlined portions:
A communications interface (Generic computer component)
A processor (Generic computer component)
A memory (Generic computer component)
However, the claimed elements fail to integrate the judicial exception into a practical application.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of a generic computer which perform generic computer functions, such as acquiring data, fails to amount to significantly more than the abstract idea. See MPEP 2106.
Dependent claims 10-16 fail to recite additional limitations compared to dependent claims 2-8 and are rejected in the same manner.
Independent claim 17 recites the following additional limitations compared to independent claim 1:
A computer-readable recording medium having recorded thereon a program comprising one or more instructions for executing, on a computer
Under Step 2A prong 2, the claim recites additional limitations in the underlined portions:
A computer-readable recording medium (Generic computer component)
However, the claimed elements fail to integrate the judicial exception into a practical application.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of a generic computer which perform generic computer functions, such as acquiring data, fails to amount to significantly more than the abstract idea. See MPEP 2106.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (Dental Biometrics: Alignment and Matching of Dental Radiographs; “CHEN”).
Regarding claim 1 (Currently Amended), CHEN teaches
A method for processing an intraoral image in an intraoral image processing apparatus, the method comprising:
receiving an intraoral image generated by scanning teeth from a server or medical device ([pg. 1319, 1. Introduction] The radiographs acquired after the victim’s death are called postmortem (PM) radiographs, and the radiographs acquired while the victim is alive are called antemortem (AM) radiographs (Fig. 1).);
aligning a teeth model template including a plurality of template teeth to teeth in the intraoral image ([pg. 1319, 3.1 Matching Tooth Contours] This algorithm aligns the contours and calculates the average distance between all points in the query shape and their closest points in the database shape and uses it to represent the distance between tooth contours.);
obtaining a plurality of tooth pieces by separating the intraoral image into a tooth mass and a gingiva according to a curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.)
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, and separating the tooth mass into a plurality of tooth pieces according to the curvature distribution, wherein each tooth is separated into two or more tooth pieces according to the curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.) ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.); and
individualizing the teeth of the intraoral image by identifying each tooth piece corresponding to each template tooth and collecting one or more tooth pieces corresponding to each of the aligned template teeth ([pg. 1319, 3. Matching at Tooth Level] A pair of neighboring teeth are viewed as a unit in matching. The contours of teeth are matched using a shape registration method, and the dental work is matched in the overlapping areas of the teeth.)
Regarding claim 2 (Currently Amended), CHEN teaches the method of claim 1,
CHEN further teaches
wherein the individualizing of the teeth of the intraoral image comprises:
obtaining a mapping relationship between each tooth piece of the intraoral image and each of the template teeth by identifying the tooth piece corresponding to each aligned template tooth ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be θ, the translations along the x and y axes be tx and ty, respectively, and the scaling be s.); and
collecting the one or more tooth pieces mapped to each template tooth by using the mapping relationship ([pg. 1320, 3.1 Matching Tooth Contours] The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; See[ Fig. 7]).
Regarding claim 3 (Currently Amended), CHEN teaches the method of claim 1,
CHEN further teaches
wherein the aligning of the teeth model template including the plurality of template teeth to the teeth in the intraoral image comprises positioning the plurality of template teeth of the teeth model template to correspond to the teeth in the intraoral image ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be , the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]).
Regarding claim 18 (New), CHEN teaches method of claim 1,
wherein the obtaining of the plurality of tooth pieces comprises: separating the tooth mass and the gingiva from each other in the intraoral image by using a first curvature threshold used for separating a boundary between the tooth mass and the gingiva (Matching Contours of Teeth); and separating the tooth mass into the plurality of tooth pieces by using a second curvature threshold used for separating a boundary of each tooth piece in the tooth mass (Matching Contours of Dental Work).
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Regarding claim 19 (New), CHEN teaches method of claim 1, wherein the second curvature threshold is the same as or different from the first curvature threshold (The extraction of tooth contours along with some preprocessing procedures, such as radiograph segmentation and gumline detection, has been discussed in the literature [1], [2], [3]. Another useful dental feature is the contours of the dental work. The dental work appears as bright regions in the radiographs (Fig. 1). The contour extraction of dental work is based on fitting a mixture of Gaussians to the intensity histograms – the Gaussian component with the largest mean corresponds to the pixel intensities associated with the dental work. The number of components in the mixture can be estimated using the algorithm proposed by Figueiredo and Jain [4]. The dental work can be segmented using a threshold which best splits the Gaussian component with the largest mean from the other components (See Fig. 3) [5], [6].).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4-5, 9-13, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Dental Biometrics: Alignment and Matching of Dental Radiographs; “CHEN”) in view of See et al. (US 2013/0325431 A1; “SEE”).
Regarding claim 4 (Currently Amended), CHEN teaches the method of claim 1,
CHEN further teaches
wherein the obtaining of the plurality of tooth pieces by separating the teeth of the intraoral image comprises separating each tooth into one or more tooth pieces according to the curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.)
CHEN fails to teach
comprising principal curvatures of a point on a surface of a 3D model representing a criterion for indicating degrees in which the surface is bent from the point in different directions
However, SEE teaches
comprising principal curvatures of a point on a surface of a 3D model representing a criterion for indicating degrees in which the surface is bent from the point in different directions ([0084] FIG. 8 shows in more detail an exemplary gingival boundary 124 partitioning the tooth surface 120 and the surrounding structure 122. In a particular embodiment, the boundary 124 is determined by probing the degree of concavity along the surface of the dentition surface 110. With the surface triangles already identified as part of the tooth surface 120 as a starting point, the computer algorithm proceeds by outwardly searching for surface triangles of the dentition surface 110 having a certain numeric characteristic. This numeric characteristic can determine, or assist in determining, the local concavity of the dentition surface 100. [0085] The algorithm proceeds to search for surface triangles that are potential boundary triangles. Potential boundary triangles satisfy a threshold local concavity, a condition that suggests that a boundary between the tooth and a surrounding structure may have been found. This threshold can be met, for example, when the numeric characteristic falls above a threshold value, below a threshold value, or within a pre-determined range of values. In a preferred embodiment, the numeric characteristic is an angle formed between the plane of the given surface triangle and the plane of one of three possible neighboring surface triangles. Alternatively, the numeric characteristic could be the internal angle, aspect ratio or overall area of the surface triangle. [0086] The above searching process is applied iteratively to identify an end-to-end chain of contiguous boundary triangles in which each boundary triangle satisfies the threshold concavity requirement. The identified series of triangles can thus define a proposed gingival boundary. The proposed gingival boundary is deemed acceptable, for example, if each boundary triangle has a numeric characteristic that satisfies a threshold value and also has a maximum of two neighbors that likewise satisfy this condition. In a preferred embodiment, the numeric characteristic is an angle of a surface triangle whose threshold value starts at 5 degrees. This condition may be used to propose a set of surface triangles for providing an acceptable gingival boundary (i.e. gingival margin). If this condition is found unacceptable, the threshold value can be iteratively changed by 0.5 degree decrements and this process repeated until an acceptable gingival boundary is provided.)
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s recognition of tooth elements in the segmentation process. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides more accurate recognition (SEE, Figure 15-17 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention, [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 5 (Currently Amended), CHEN teaches the intraoral image processing method of claim 1,
CHEN further teaches
wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using an orientation condition of each template tooth and each tooth piece ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be , the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]).
CHEN fails to teach
wherein the orientation condition represents a condition for identifying the tooth piece corresponding to the template tooth by using a normal vector of the template tooth and a normal vector of the tooth piece
However, SEE teaches
wherein the orientation condition represents a condition for identifying the tooth piece corresponding to the template tooth by using a normal vector of the template tooth and a normal vector of the tooth piece ([0099] As an alternative to using landmarks 140, a "single-click" method may be used to define the coordinate system. This method uses point input data that defines a point on the virtual tooth, receiving axis input data that defines first and second axes associated with the virtual tooth, computing a substantially normal vector for a portion of the tooth surface surrounding the point, and computing the tooth coordinate system based on the axis input and the computed vector. This is particularly useful for determining coordinate axes for incisor, cuspid, and bicuspid teeth, for which the buccolabial surfaces display a relatively small degree of variability. In FIGS. 15-17, this implementation is used for the upper right central tooth surface 152. The creation of the coordinate system can be performed semi-automatically, as will be described below…[0104])
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s recognition of tooth elements in the segmentation process. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides more accurate recognition (SEE, Figure 15-17 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention see also [0066], [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 9 (Currently Amended), CHEN teaches
obtain an intraoral image generated by scanning teeth from a server or a medical device ([pg. 1319, 1. Introduction] The radiographs acquired after the victim’s death are called postmortem (PM) radiographs, and the radiographs acquired while the victim is alive are called antemortem (AM) radiographs (Fig. 1).);
align a teeth model template including a plurality of template teeth to teeth in the intraoral image ([pg. 1319, 3.1 Matching Tooth Contours] This algorithm aligns the contours and calculates the average distance between all points in the query shape and their closest points in the database shape and uses it to represent the distance between tooth contours.);
obtain a plurality of tooth pieces by separating the intraoral image into a tooth mass and a gingiva according to a curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.)
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, and separating the tooth mass into a plurality of tooth pieces according to the curvature distribution, wherein each tooth is separated into two or more tooth pieces according to the curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.) ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.); and
individualize the teeth of the intraoral image by identifying each tooth piece corresponding to each template tooth and collecting one or more tooth pieces corresponding to each of the aligned template teeth ([pg. 1319, 3. Matching at Tooth Level] A pair of neighboring teeth are viewed as a unit in matching. The contours of teeth are matched using a shape registration method, and the dental work is matched in the overlapping areas of the teeth.)
CHEN fails to teach
An intraoral image processing device comprising a communications interface, a processor and a memory, wherein the processor is configured to execute one or more instructions stored in the memory
However, SEE teaches
An intraoral image processing device comprising a communications interface, a processor and a memory ([0058] FIG. 1A is a block diagram illustrating an exemplary computer environment 10 that includes a client computer 12. Preferably, the client computer 12 has a processor, input device, memory, and display device. The client computer 12 presents an environment for an orthodontic practitioner 14 to interact with a digital representation of a portion of or an entire dental arch of patient 16 to generate and visualize an orthodontic digital setup for patient 16. Optionally and as shown, the client computer 12 communicates with a manufacturing facility 18 via network 20...),
wherein the processor is configured to execute one or more instructions stored in the memory ([0059] The client computer is operated by a user. The user interacts with modeling software executing on a computer to visualize, process, and manipulate the 3D representation of a patient's dental arch, arches, or subsets thereof...) to:
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s an intraoral image processing device comprising a processor and a memory, wherein the processor is configured to execute one or more instructions stored in the memory to. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides a computing device related, system, and method embodiments for intraoral imaging (SEE, Figure 1 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention, [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 10 (Original), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to individualize the teeth of the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to:
obtain a mapping relationship between each tooth piece of the intraoral image and each of the template teeth by identifying the tooth piece corresponding to each aligned template tooth ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be θ, the translations along the x and y axes be tx and ty, respectively, and the scaling be s.); and
collect the one or more tooth pieces mapped to each template tooth by using the mapping relationship ([pg. 1320, 3.1 Matching Tooth Contours] The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; See[ Fig. 7]).
Regarding claim 11 (Original), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to align the teeth model template including the plurality of template teeth to the teeth in the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to position the plurality of template teeth of the teeth model template to correspond to the teeth in the intraoral image ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be , the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]).
Regarding claim 12 (Currently Amended), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to obtain the plurality of tooth pieces by separating the teeth of the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to separate each tooth into one or more tooth pieces by separating the teeth according to the curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.).
CHEN fails to teach
comprising principal curvatures of a point on a surface of a 3D model representing a criterion for indicating degrees in which the surface is bent from the point in different directions
However, SEE teaches
comprising principal curvatures of a point on a surface of a 3D model representing a criterion for indicating degrees in which the surface is bent from the point in different directions ([0084] FIG. 8 shows in more detail an exemplary gingival boundary 124 partitioning the tooth surface 120 and the surrounding structure 122. In a particular embodiment, the boundary 124 is determined by probing the degree of concavity along the surface of the dentition surface 110. With the surface triangles already identified as part of the tooth surface 120 as a starting point, the computer algorithm proceeds by outwardly searching for surface triangles of the dentition surface 110 having a certain numeric characteristic. This numeric characteristic can determine, or assist in determining, the local concavity of the dentition surface 100. [0085] The algorithm proceeds to search for surface triangles that are potential boundary triangles. Potential boundary triangles satisfy a threshold local concavity, a condition that suggests that a boundary between the tooth and a surrounding structure may have been found. This threshold can be met, for example, when the numeric characteristic falls above a threshold value, below a threshold value, or within a pre-determined range of values. In a preferred embodiment, the numeric characteristic is an angle formed between the plane of the given surface triangle and the plane of one of three possible neighboring surface triangles. Alternatively, the numeric characteristic could be the internal angle, aspect ratio or overall area of the surface triangle. [0086] The above searching process is applied iteratively to identify an end-to-end chain of contiguous boundary triangles in which each boundary triangle satisfies the threshold concavity requirement. The identified series of triangles can thus define a proposed gingival boundary. The proposed gingival boundary is deemed acceptable, for example, if each boundary triangle has a numeric characteristic that satisfies a threshold value and also has a maximum of two neighbors that likewise satisfy this condition. In a preferred embodiment, the numeric characteristic is an angle of a surface triangle whose threshold value starts at 5 degrees. This condition may be used to propose a set of surface triangles for providing an acceptable gingival boundary (i.e. gingival margin). If this condition is found unacceptable, the threshold value can be iteratively changed by 0.5 degree decrements and this process repeated until an acceptable gingival boundary is provided.)
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s recognition of tooth elements in the segmentation process. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides more accurate recognition (SEE, Figure 15-17 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention, [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 13 (Currently Amended), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to individualize the teeth of the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to identify each tooth piece corresponding to each template tooth by using an orientation condition of each template tooth and each tooth piece ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be θ, the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]).
CHEN fails to teach
wherein the orientation condition represents a condition for identifying the tooth piece corresponding to the template tooth by using a normal vector of the template tooth and a normal vector of the tooth piece
However, SEE teaches
wherein the orientation condition represents a condition for identifying the tooth piece corresponding to the template tooth by using a normal vector of the template tooth and a normal vector of the tooth piece ([0099] As an alternative to using landmarks 140, a "single-click" method may be used to define the coordinate system. This method uses point input data that defines a point on the virtual tooth, receiving axis input data that defines first and second axes associated with the virtual tooth, computing a substantially normal vector for a portion of the tooth surface surrounding the point, and computing the tooth coordinate system based on the axis input and the computed vector. This is particularly useful for determining coordinate axes for incisor, cuspid, and bicuspid teeth, for which the buccolabial surfaces display a relatively small degree of variability. In FIGS. 15-17, this implementation is used for the upper right central tooth surface 152. The creation of the coordinate system can be performed semi-automatically, as will be described below…[0104])
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s recognition of tooth elements in the segmentation process. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides more accurate recognition (SEE, Figure 15-17 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention see also [0066], [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 17 (Currently Amended), CHEN teaches
receive an intraoral image generated by scanning teeth from a server or a medical device ([pg. 1319, 1. Introduction] The radiographs acquired after the victim’s death are called postmortem (PM) radiographs, and the radiographs acquired while the victim is alive are called antemortem (AM) radiographs (Fig. 1).);
align a teeth model template including a plurality of template teeth to teeth in the intraoral image ([pg. 1319, 3.1 Matching Tooth Contours] This algorithm aligns the contours and calculates the average distance between all points in the query shape and their closest points in the database shape and uses it to represent the distance between tooth contours.);
obtain a plurality of tooth pieces by separating the intraoral image into a tooth mass and a gingiva according to a curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.)
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, and separating the tooth mass into a plurality of tooth pieces according to the curvature distribution, wherein each tooth is separated into two or more tooth pieces according to the curvature distribution ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.) ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.); and
individualize the teeth of the intraoral image by identifying each tooth piece corresponding to each template tooth and collecting one or more tooth pieces corresponding to each of the aligned template teeth ([pg. 1319, 3. Matching at Tooth Level] A pair of neighboring teeth are viewed as a unit in matching. The contours of teeth are matched using a shape registration method, and the dental work is matched in the overlapping areas of the teeth.)
CHEN fails to teach
A non-transitory computer-readable recording medium having recorded thereon a program comprising one or more instructions which are executed by a processor of an intraoral image processing device to cause the intraoral image processing device to:
However, SEE teaches
A non-transitory computer-readable recording medium having recorded thereon a program comprising one or more instructions ([0059] The client computer is operated by a user. The user interacts with modeling software executing on a computer to visualize, process, and manipulate the 3D representation of a patient's dental arch, arches, or subsets thereof...) which are executed by a processor ([0058] FIG. 1A is a block diagram illustrating an exemplary computer environment 10 that includes a client computer 12. Preferably, the client computer 12 has a processor, input device, memory, and display device. The client computer 12 presents an environment for an orthodontic practitioner 14 to interact with a digital representation of a portion of or an entire dental arch of patient 16 to generate and visualize an orthodontic digital setup for patient 16. Optionally and as shown, the client computer 12 communicates with a manufacturing facility 18 via network 20...) of an intraoral image processing device to cause the intraoral processing device to…
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s an intraoral image processing device comprising a processor and a memory, wherein the processor is configured to execute one or more instructions stored in the memory to. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides a computing device related, system, and method embodiments for intraoral imaging (SEE, Figure 1 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention, [0004] When raw data representing the shapes of teeth are received by a computer, the data is often little more than a point cloud in three dimensional (3D) space. Typically, this point cloud is surfaced to create 3D object models of the patient's dentition, including one or more teeth, gingival tissue, and other surrounding oral structure. In order for this data to be useful in orthodontic diagnosis and treatment, the dentition surface is generally "segmented" to produce one or more discrete, movable 3D tooth object models representing individual teeth. It is also preferred that these tooth models are separated from the gingiva into separate objects.).
Regarding claim 20 (New), CHEN in view of SEE teaches method of claim 1,
CHEN fails to teach
wherein each tooth is separated into the one or more tooth pieces by cutting and segmenting, into the pieces, a portion having a value of the distribution min (k1, k2) which is less than a threshold, wherein the value of the distribution min(kl, k2) indicates a less value of the principal curvatures kl and k2 assessed on each vertex of a mesh structure forming the intraoral image
However, SEE teaches
wherein each tooth is separated into the one or more tooth pieces by cutting and segmenting, into the pieces (Definition of Coordinate Systems discussion, see especially tooth surfaces), a portion having a value of the distribution min (k1, k2) which is less than a threshold (satisfies the threshold with maximum of two neighbors, see discussion of the boundary triangles [0085]), wherein the value of the distribution min (kl, k2) indicates a less value of the principal curvatures kl and k2 assessed on each vertex of a mesh structure (concavities with the definition surfaces of the triangular mesh structures used for assessing the boundary target, Figure 8 discussions) forming the intraoral image (Tooth Segmentation section, Root Stub Formation section, see especially [0108] and [0084]-[0086])
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s individualize the teeth with the teaching of SEE’s recognition of tooth elements in the segmentation process. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teaching provides more accurate recognition (SEE, Figure 15-17 description) for the proper recognition of tooth objects in the segmentation process (SEE; Summary of the Invention see also [0066]).
Claims 6-8 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Dental Biometrics: Alignment and Matching of Dental Radiographs; “CHEN”) in view of SEE et al. (US 20160070821 A1; “SEE”) and further in view of Seshagiri et al. (COMPUTING GEOMETRIC TRANSFORMATIONS OF IRREGULAR TEETH SETS FOR ORTHODONTIC TREATMENT; “SESHAGIRI”).
Regarding claim 6 (Currently Amended), CHEN teaches the method of claim 5,
wherein the identifying of each tooth piece corresponding to each template tooth by using the orientation condition comprises:
CHEN fails to teach
determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value
However, SEE teaches
determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value (Figure 2A and Figure 19).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
identifying a crossing point at which a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece
However, SESHAGIRI teaches
identifying a crossing point at which a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing method with the teaching of SESHAGIRI’s identifying a crossing point at which a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Regarding claim 7 (Currently Amended), CHEN teaches the method of claim 1,
CHEN further teaches
wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using a distance condition of each template tooth and each tooth piece ([pg. 1320, 3.2 Matching Dental Work] Given two images M and N; [pg. 1321, 3.2 Matching Dental Work] If the dental works in images M and N have similar shapes and are well aligned, then nmp is small. So, the distance between the dental work in images M and N is defined as [Eq. 4]), and
CHEN fails to teach
determining that the tooth piece corresponds to the template tooth based on a distance from the vertex to the crossing point being less than a threshold value
However, SEE teaches
determining that the tooth piece corresponds to the template tooth based on a distance from the vertex to the crossing point being less than a threshold value (Determining Translation/Rotations Section, starting [0141], Moving Selected Tooth out of Collision with Neighbors, starting [0157] and finally, Move Arch out of Intersection with Opposing Arch, starting [0196]).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
the identifying of each tooth piece corresponding to each template tooth comprises: identifying a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece
However, SESHAGIRI teaches
the identifying of each tooth piece corresponding to each template tooth comprises: identifying a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing method with the teaching of SESHAGIRI’s the identifying of each tooth piece corresponding to each template tooth comprises: identifying a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Regarding claim 8 (Currently Amended), CHEN teaches the intraoral image processing method of claim 1,
CHEN further teaches
wherein the individualizing of the teeth of the intraoral image comprises identifying each tooth piece corresponding to each template tooth by using a distance condition ([pg. 1320, 3.2 Matching Dental Work] Given two images M and N; [pg. 1321, 3.2 Matching Dental Work] If the dental works in images M and N have similar shapes and are well aligned, then nmp is small. So, the distance between the dental work in images M and N is defined as [Eq. 4]) and an orientation condition of each template tooth and each tooth piece ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be θ, the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]), and
CHEN fails to teach
determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value
However, SEE teaches
determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value ([0085]-[0086], Determining Translation/Rotations Section, starting [0141], Moving Selected Tooth out of Collision with Neighbors, starting [0157] and finally, Move Arch out of Intersection with Opposing Arch, starting [0196]).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
the identifying of each tooth piece corresponding to each template tooth by using the distance condition and the orientation condition comprises: identifying, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex
However, SESHAGIRI teaches
the identifying of each tooth piece corresponding to each template tooth by using the distance condition and the orientation condition comprises: identifying, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing method with the teaching of SESHAGIRI’s the identifying of each tooth piece corresponding to each template tooth by using the distance condition and the orientation condition comprises: identifying, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Regarding claim 14 (Original), CHEN in view of SEE teaches the intraoral image processing device of claim 13,
SEE further teaches
determine that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value(Figure 2A and Figure 19).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
wherein, in order to identify each tooth piece corresponding to each template tooth by using the orientation condition, the processor is further configured to execute the one or more instructions stored in the memory to: identify a crossing point at a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece
However, SESHAGIRI teaches
wherein, in order to identify each tooth piece corresponding to each template tooth by using the orientation condition, the processor is further configured to execute the one or more instructions stored in the memory to: identify a crossing point at a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing device with the teaching of SESHAGIRI’s wherein, in order to identify each tooth piece corresponding to each template tooth by using the orientation condition, the processor is further configured to execute the one or more instructions stored in the memory to: identify a crossing point at a normal vector on each vertex of a three-dimensional (3D) mesh forming the template tooth intersects a 3D mesh forming the tooth piece. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Regarding claim 15 (Original), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to individualize the teeth of the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to: identify each tooth piece corresponding to each template tooth by using a distance condition of each template tooth and each tooth piece ([pg. 1320, 3.2 Matching Dental Work] Given two images M and N; [pg. 1321, 3.2 Matching Dental Work] If the dental works in images M and N have similar shapes and are well aligned, then nmp is small. So, the distance between the dental work in images M and N is defined as [Eq. 4]), and
SEE further teaches
determine that the tooth piece corresponds to the template tooth, based on a distance from the vertex to the crossing point being less than a threshold value (Determining Translation/Rotations Section, starting [0141], Moving Selected Tooth out of Collision with Neighbors, starting [0157] and finally, Move Arch out of Intersection with Opposing Arch, starting [0196]).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
in order to identify each tooth piece by using the distance information, identify a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece
However, SESHAGIRI teaches
in order to identify each tooth piece by using the distance information, identify a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing device with the teaching of SESHAGIRI’s in order to identify each tooth piece by using the distance information, identify a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Regarding claim 16 (Original), CHEN in view of SEE teaches the intraoral image processing device of claim 9,
CHEN further teaches
wherein, in order to individualize the teeth of the intraoral image, the processor is further configured to execute the one or more instructions stored in the memory to:
identify each tooth piece corresponding to each template tooth by using a distance condition ([pg. 1320, 3.2 Matching Dental Work] Given two images M and N; [pg. 1321, 3.2 Matching Dental Work] If the dental works in images M and N have similar shapes and are well aligned, then nmp is small. So, the distance between the dental work in images M and N is defined as [Eq. 4]) and an orientation condition of each template tooth and each tooth piece ([pg. 1320, 3.1 Matching Tooth Contours] A transformation is used to align the contours. Let the rotation angle be , the translations along the x and y axes be tx and ty, respectively, and the scaling be s. A point (x,y)^t is transformed to T(x,y), where [see Eq. 1] . The optimal transformation T minimizes the matching distance, which is computed after shape registration. To initialize T, both A and B are normalized so that their bounding boxes have the same widths (Fig. 7c).; see [Fig. 7]);
SEE further teaches
determine that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value ([0085]-[0086]Determining Translation/Rotations Section, starting [0141], Moving Selected Tooth out of Collision with Neighbors, starting [0157] and finally, Move Arch out of Intersection with Opposing Arch, starting [0196]).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN’s intraoral image processing method with the teaching of SEE’s determining that the tooth piece corresponds to the template tooth, based on an angle between a normal vector on the identified crossing point with respect to the tooth piece and the normal vector on the vertex being less than a threshold value. The motivation to combine the teachings of CHEN and SEE is because including SEE’s teachings reflect the defined coordinate system of each tooth and help a user visualize the orientation of each tooth with respect to its neighbors (SEE discussion Root Stub Formation beginning [0106]).
CHEN in view of SEE fails to teach
in order to identify each tooth piece by using the distance information and the orientation condition, identify, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex
However, SESHAGIRI teaches
in order to identify each tooth piece by using the distance information and the orientation condition, identify, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex ([pg. 6, Alignment of Teeth] Aligning the normal vector of the tooth surface to be parallel to the normal of the template curve constrains the orientation of the tooth.); and
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of CHEN in view of SEE’s intraoral image processing device with the teaching of SESHAGIRI’s in order to identify each tooth piece by using the distance information and the orientation condition, identify, as a crossing point at which a normal vector on each vertex of a 3D mesh forming the template tooth intersects a 3D mesh forming the tooth piece, the crossing point within a predetermined distance from the vertex. The motivation to combine the teachings of CHEN in view of SEE and SESHAGIRI is because including the teaching of SESHAGIRI allows for automatically aligning a model of a patient’s teeth to an ideal arch (SESHAGIRI [pg. 8, Conclusions and Future Work]).
Response to Arguments
Claim Rejection Under 35 USC § 101
The Examiner most respectfully disagrees with Applicant’s assertion that (1) The underlying idea in the claims is not merely an abstract idea because the 2019 Revised Patent Subject Matter Eligibility Guidance released by the USPTO on January 4, 2019 (the "2019 Revised Guidance") changes Step 2A of the Alice/Mayo test by including a two-prong procedure. Under Prong One of Step 2A, only the following groupings of subject matter are considered as abstract ideas: a) mathematical concepts; b) certain methods of organizing human activity; and c) mental processes. The cited rejection is directed to both mental processes and mathematical concepts, and thus Applicant is asked to please provide said guidance, and please clarify how in being directed to these concepts these concepts are not the very ideas discussed in MPEP 2106.07.
As to Applicant’s argument that under Prong Two of Step 2A, the January 2019 Guidance explains that a claim is not "directed to" a judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception, Applicant is invited to show this practical application in the presently claimed limitations with citations from the originally filed specification in order to overcome the rejection.
Again, to Applicant’s assertion that in addition, under Prong Two of Step 2A, even assuming that claim 1 recites any alleged judicial exception, the alleged judicial exception is integrated into a practical application of the exception, the Examiner kindly invites Applicants to show where each limitation is integrated into a practical application as discussed in the specification, using the guidance of MPEP 2106.07(b) and 2016.05.
Claim Rejection Under 35 USC § 102
The Examiner most respectfully disagrees with Applicant’s assertion that because the Office pointed out sections that only pertain to the method of matching contours by treating two adjacent teeth as one unit that the citations are unrelated to the subject matter of claim 1 of the present application, which individualizes each tooth by collecting tooth pieces corresponding to a template tooth. Applicants are directed to the full discussion of Chen, where the individual tooth is collected for template:
[pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.) ([pg. 1319, 2. Feature Extraction] The methods for extraction of tooth contours along with some preprocessing procedures, i.e., radiograph segmentation and gumline detection, have been presented in the literature [4], [5], [6]. We use the active contour model for extraction of the tooth contours [5]. Fig. 4 shows some examples of the extracted tooth contours.
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[pg. 1319, 3. Matching at Tooth Level] A pair of neighboring teeth are viewed as a unit in matching. The contours of teeth are matched using a shape registration method, and the dental work is matched in the overlapping areas of the teeth. As can be seen from the citation, each individual tooth is recognized, the matching is simply done in pairs for ease of computation, which discusses the computational nature of the algorithm used, not the segmentation process where each tooth is individualized and even dental work recognized (see discussion in relation to the Gaussian modeling of Figures 5 and 6) to differentiate between individual teeth. The system of Chen provides identical functionality to that of the present invention, and therefore the rejection is maintained.
Claim Rejection Under 35 USC § 103
The Examiner acknowledges the remarks made to the combination of Khardekar and Seshagiri, in Applicant’s assertion that the prior art combinations do not make up for the elements of claim 1 that are missing in Chen, thus, Applicant submits that claims 9 and 17 are patentable for reasons similar to those submitted for claim 1. The Examiner notes that no specific elements have been discussed as required by 37 CFR 1.111(b) and thus the arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicant has additionally not provided points of novelty as per 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Therefore, the rejections of the independent and dependent claims are most respectfully maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Somasundaram et al. (U.S. Patent Application Publication US 20160070821 A1):
Abstract
Methods for aligning a digital 3D model of teeth represented by a 3D mesh to a desired orientation within a 3D coordinate system. The method includes receiving the 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis in the 3D coordinate system. The methods can also detect a gum line in the digital 3D model to remove the gingiva from the model.
Background/Summary
[0001] The use of digital 3D models in the dental market is becoming more prevalent. These models can be acquired in vivo using an intra-oral scanner or off-line by laser scanning of a traditional impression. The digital 3D models can be used tom various clinical tasks including treatment planning, and crown and implant preparation. The models can also be used in diagnostic aides, for example to assess tooth wear and gingival recession. The digital 3D models are usually obtained in a random orientation and not fixed to a particular coordinate system. Accordingly, a need exists to align intra-oral digital 3D models to a given coordinate system for diagnostic or other purposes.
[0002] Methods for aligning a digital 3D model of teeth, consistent with the present invention, include receiving a digital 3D model of teeth represented by a 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis within a 3D coordinate system.
[0003] Methods for modifying and aligning a digital 3D model of teeth, consistent with the present invention, include receiving a digital 3D model of teeth with associated gingiva represented by a 3D mesh in random alignment, detecting a gum line in the digital 3D model, and removing the gingiva from the digital 3D model. The digital 3D model without the gingiva is aligned with a desired axis within a 3D coordinate system.
Description
[0005] FIG. 1 is a diagram of a system for aligning digital 3D models based upon intra-oral 3D scans or 3D scans from impressions;
[0006] FIG. 2 illustrates a 3D model of teeth from intra-oral scans;
[0007] FIG. 3 is a flow chart of methods for aligning and modifying digital 3D models; and
[0008] FIG. 4 is a diagram of a digital 3D model aligned in a desired orientation within a 3D coordinate system and with the gum line detected.
[0009] FIG. 1 is a diagram of a system 10 for aligning digital 3</b>D models based upon intra-oral 3D scans. System 10 includes a processor 20 receiving digital 3D models of teeth (12) from intra-oral 3D scans or scans of impressions of teeth. System 10 also includes an electronic display device 16, such as a liquid crystal display (LCD) device, for displaying digital 3D models and an input device 18 for receiving user commands or other information. An example of digital 3D model of a patient's teeth from a scan is shown in FIG. 2. Systems to generate digital 3D images or models based upon image sets from multiple views are disclosed in U.S. Pat. Nos. 7,956,862 and 7,605,817, both of which are incorporated herein by reference as if full set forth. These systems can use an intra-oral scanner to obtain digital images from multiple views of teeth or other intra-oral structures, and those digital images are processed to generate a digital 3D model representing the scanned teeth. System 10 can be implemented with, for example a desktop, notebook, or tablet computer. System 10 can receive the 3D scans locally or remotely via a network.
[0010] The 3D scans addressed herein are represented as triangular meshes. The triangular mesh is common representation of 3D surfaces and has two components. The first component, referred to as the vertices of the mesh, are simply the coordinates of the 3D points that have been reconstructed on the surface i.e., a point cloud. The second component, the mesh faces, encodes the connections between points on the object and is an efficient way of interpolating between the discrete sample points on the continuous surface. Each face is a triangle defined by three vertices, resulting in a surface that can be represented as a set of small triangular planar patches.
[0011] FIG. 3 is a flow chart of methods 1-4 for aligning digital 3D models and optionally removing the gingiva from the models. These methods can be implemented in software or firmware modules, for example, for execution by processor 20. These methods can alternatively be implemented in hardware modules or a combination of software and hardware. The methods 1-4 receive a 3D mesh in random alignment (step 30) and generate a 3D mesh aligned with a desired axis in a 3D coordinate system (step 32). The 3D mesh is triangular mesh having the components described above.
[0012] In one particular embodiment, the alignment results in an occlusal plane being aligned to a desired orientation within a 3D coordinate system. The occlusal plane can be determined by finding points at the top of a tooth or teeth in a digital 3D model of the teeth and fitting a plane to those points. In one example, a desired orientation aligns the occlusal plane with the Y axis with the teeth pointing up in the model, although the occlusal plane can also be aligned with other axes using the alignment methods. An example of a 3D coordinate system includes an X axis, a Y axis, and Z axis with each of the axes being mutually orthogonal with one another.
[0013] Method 1 involves the following steps: compute the normals of the mesh at each face or vertex, or at a subset of the faces or vertices (step 34); compute an aggregate of the surface normals to determine a representative normal direction by calculating the mean of the surface normals or, alternatively, calculating the sum or the median of the surface normals (step 36) and compute and apply a rotation matrix to align the mean normal with a desired axis (step 38). There are several methods that can compute the rotation matrix between two vectors, the mean of the normals with the desired spatial orientation. The exemplary method below uses Rodrigues formula.
Alignment Method 2—Open Mesh Assumption
[018] Method 2 involves the following steps: compute a grid of vectors aligned with the desired direction of orientation (step 40); project the vectors through the mesh and count the number of intersections with mesh faces (step 42); compute the cost function as the total number of cases, where the number of intersections for each grid line with faces on the mesh is one (step 44); and rotate the model and repeat steps 42 and 44 until the cost function is maximized (step 46).
[0019] This approach makes the assumption that the model represents an open topographic surface, i.e. the surface does not self-intersect or close on itself. This model is often the ease with 3D scans of dentitions. To compute the final transformation matrix, this method projects a set of parallel rays (also parallel to a fixed desired axis) onto the mesh; the grid spacing of those rays is chosen based on the model resolution and computational limitations. The cost function is a ratio of the number of rays that intersected one face to the number of rays that intersected more than one face:
[0022] This alignment method can be implemented using an SVR method to find the occlusal plane fitted to a mesh of the teeth in the digital 3D model. The alignment can be used to have the teeth in the digital 3D model essentially aligned with the Y axis. The alignment can use the LIBSVM toolbox and ε−SVR method. The kernel is chosen to be linear and ε5. The best value of epsilon can be chosen based on many training meshes. The training is based on the assumption that teeth are roughly pointing up along the Y axis. The output is sample points from the occlusal plane which is given to a simple PCA method to find the normal direction. Alternatively, the SVR can also directly return the equation of the plane ca best fit. The normal direction can then be computed from this plane. SVR uses a linear loss function with a zero part within the margins which performs better for teeth dataset than the quadratic loss function in regular least square regression methods. It helps to decrease the effect of gingiva cut-lines which can be very jagged and bumpy in mesh scans. It also tries to rule out the vertical points on the teeth (buccal part) and give more weight of importance to the horizontal points on teeth (cuspal part) M determining the occlusal plane orientation. The RANSAC method and Robust PCA method can alternatively be used for the alignment.
[0024] Method 4 involves the following steps: detect the gum line in the model using correlation on curvature or a classification function based upon mesh surface properties (step 54): fit a surface to the gum line points using a modified ridge estimator (step 56); remove all vertices below the surface corresponding to the gingiva (step 58); and align the remaining model using any of the above methods 1-3 (step 60). This method 4 can align the model before detecting the gum line and realign the model after guru line detection, or only align the model after gum line detection.
[0025] For step 54, alternatively a classifier using multiple surface features such as curvature, normal direction, mesh local covariance, or other such features can also be used to predict if a vertex lies on the gum line. Some examples of the classifiers are linear discriminant classifier, decision tree, support vector machines, and the like.
surface to gum line points. 7 Remove portions below the surface (gingiva in the model) that intersect the mesh. 8 Realign the model with gingiva removed using aggregation of normals (method 1) or other alignment methods.
[0028] FIG. 4 is a diagram of a digital 3D model aligned with a desired orientation and with the gum fine detected. For example, the digital 3D model of teeth 70 has been aligned with the Y axis (teeth pointing up along with Y axis), and the gum line 72 has been identified with portions of the gingiva below gum fine 72 removed from the digital 3D model. In this example, the alignment results in a desired orientation of the occlusal plane generally parallel with or extending along, the XZ plane and perpendicular to, or intersecting with, the Y axis..
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily C Terrell whose telephone number is (571)270-3717. The examiner can normally be reached Monday - Thursday 7 a.m.-4 p.m..
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/EMILY C TERRELL/ Supervisory Patent Examiner, Art Unit 2666
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