DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed June 9th, 2026 has been entered. Claims 1-20 remain pending in the application. Claims 1, 11 and 12 have been amended. The Examiner acknowledges the corrections to the Specification as outlined in the previous action, and the associated objections to the Specification are withdrawn. The Examiner notes that although the Applicant asserts that Claims 5, 6, 8, 9, 16, 17, 19 and 20 have been amended to include actual corresponding definitions of the terms directly from the specification, the aforementioned claims have not been amended.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 5, 6, 8, 9, 16, 17, 19 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 5 recites a “first preset direction”, which the specification makes reference to in Figure 12. It is depicted as originating from the coordinate origin. However, it is unclear what exactly “first preset direction” is in context with the claimed invention. “first preset direction” is only defined in vague terminology (Paragraph [0094]: “The aforementioned first preset direction may be the preset direction. By identifying the point cloud data of the rotated dental model, the first preset direction corresponding to the rotated dental model may be obtained”). Notably, a first preset direction is defined as being the preset direction, which itself is not concretely defined. Beyond the vague definition put forth in the Specification, the preset direction is also defined in relation to other elements, most notably in Paragraph [0011], which defines it as “corresponding to the first category by using the preset correspondence”. Both “category and correspondence” are similarly lacking definition with regards to the claimed invention. Furthermore, while Paragraph [0095] describes a numerical example for a first preset direction (Paragraph [0095]: “for example, if the first preset direction is 20
°
and the second preset orientation is 40
°
, and based on the preset rotation relationship, that is, rotating the first preset direction by 20
°
, the rotated dental model in the first preset direction may be adjusted to the second preset orientation”), the Examiner notes that simply referring to direction and orientation in terms of degrees is insufficient to describe a rotation basis or orientation basis for 3D orientation, to which the Applicant’s invention pertains to, as supported in Paragraph [0095]: “The DGCNN model may be configured to recognize the rotated dental model, determine the first preset direction of the rotated dental model in the 3D space, and adjust the rotated dental model to the second preset orientation based on the first preset direction of the rotated dental model in the 3D space” and as described in Figure 12. A single value in degrees is an inadequate measure of describing 3D orientations of models, as degrees describe a direction with respect to a plane defined by 2 axis. While a preset direction is related to an associated value in degrees, Simply stating the value in degrees as a preset direction is insufficient, since there is no elaboration on the vector direction of the preset direction. While it can be argued that the preset direction is towards a specific orientation, the orientation itself similarly vaguely defined within the specifications. Although the broadest reasonable interpretation of orientation is more obvious, the definition and example of a preset direction is only clear when the orientation is defined in terms of degrees, which while apparent in Paragraph [0095], is insufficient for describing orientation of a 3D model in isolation, as stated previously.
The nature of the invention is centered around the movement and orientation of a 3D dental model in the form of a 3D point cloud within a 3D space. While a “first preset direction” is understandable with regards to a general movement of a 3D dental model, it is unclear in what way a dental model is linked to a “first preset direction”, considering Figure 12 does not display a dental model. The breadth of the claims become unclear due to the lack of definition with regards to a “first preset direction”, which can vary significantly in interpretation. Within the applicant’s specification, both preset direction and orientation are described as being a value in degrees (Paragraph [0095]), which further confuses the distinction between orientation and preset direction.
There exists significant prior art in translating and rotating point clouds (which may be dental models) to align to particular orientations using feature extraction from the point clouds, and subsequently computing translation and rotation matrices from the 3D correspondences of the point clouds. However, “direction” as a broad term is not prevalent amongst relevant prior art. While “direction” can potentially be interpreted as related in some form to translation or rotation matrices for orienting point clouds, there is no significant indication within the specification that this is the case.
A person having ordinary skill in the art could interpret “a first preset direction” as a general term for how to move a model in an initial orientation to a second orientation. However, the fact that a “first preset direction” is not explicitly or implicitly linked to a known method for doing so would make it difficult for a person having ordinary skill in the art to understand exactly what a “first preset direction” is in context with the invention.
Given that orienting point clouds is well established in the art, it wouldn’t necessarily be unreasonable for known methods to be applied to the understanding and application of a “first preset direction”, however, considering how a “first preset direction” is intrinsically linked to other elements that are similarly lacking definition such as “category” and “display direction”, doubt is cast on the assumed definition of a “first preset direction”.
While the specification does provide ample description as to how a “first preset direction” is linked to other elements such as “category” and “display direction”, it lacks a baseline description and definition of said elements, resulting in an unclear base of understanding of how said limitations are to be understood with regards to the invention as a whole.
A lack of working examples with regards to the claimed invention in context with a “first preset direction” and associated undefined elements results in an unclear understanding of the claimed invention.
As a result of the aforementioned analysis, undue experimentation would be required to make the invention based on the contents of the disclosure. For the purposes of examination, a “first preset direction” will be interpreted under the broadest reasonable interpretation of translation and/or rotation matrices.
Claim 6 recites a “first display direction” and “first category”, which are referenced in Figure 12 of the specification, where Paragraph [0096] describes the “first display direction” as belonging to the “first category”, where the “preset direction” also corresponds to the “first category”. As noted above in the analysis of Claim 5, a “first display direction” is vaguely defined, and is tangentially defined in relation to other similarly undefined elements. Similarly, a “first category” is vaguely defined. While the Applicant describes the “first category” in Paragraph [0098] as “represent[ing] the direction of the dental model”, direction is vaguely defined within the context of the invention. While the Specification does state in Paragraph [0097] that “the aforementioned first display direction may be the actual display orientation of the rotated dental model in the 3D space”, orientation is not concretely defined within the specification. Although the broadest reasonable interpretation of orientation is more obvious, the definition and example of a direction is only clear when the orientation is defined in terms of degrees, which while apparent in Paragraph [0095], is insufficient for describing orientation of a 3D model in isolation, as stated previously. Furthermore, the distinction between direction and orientation is unclear in view of the example of Paragraph [0095], and in the aforementioned passage of Paragraph [0097].
The nature of the invention is centered around the movement and orientation of a 3D dental model in the form of a 3D point cloud within a 3D space. While a “first display direction” and “first category” is understandable with regards to a general orientation and description of 3D space, it is unclear in what way a dental model is linked to a “first display direction” and “first category”, considering Figure 12 does not display a dental model. The breadth of the claims become unclear due to the lack of definition with regards to a “first display direction” and “first category”, which can vary significantly in interpretation.
There exists significant prior art in translating and rotating point clouds (which may be dental models) to align to particular orientations using feature extraction from the point clouds, and subsequently computing translation and rotation matrices from the 3D correspondences of the point clouds. However, “display direction” and “category” as broad terms are not prevalent amongst relevant prior art. While “display direction” and “category” can potentially be interpreted as related in some form to initial orientation and some form of breakdown of a 3D space in relation to the coordinate origin, the meaning becomes unclear when considered alongside “direction”.
A person having ordinary skill in the art could interpret “display direction” and “category” as general terms for orientation and coordinate ranges. However, the fact that a “display direction” and “category” are not explicitly or implicitly linked to a known method for doing so would make it difficult for a person having ordinary skill in the art to understand exactly what “display direction” and “category” are in context with the invention.
Given that orienting point clouds is well established in the art, it wouldn’t necessarily be unreasonable for known methods to be applied to the understanding and application of a “display direction” and “category”. However, considering how a “display direction” and “category” is intrinsically linked to other elements that are similarly lacking definition, doubt is cast on the assumed definitions of a “display direction” and “category”.
While the specification does provide ample description as to how a “display direction” and “category” are linked to other elements such as “direction”, it lacks a clear, baseline description and definition of said elements, resulting in an unclear base of understanding of how said limitations are to be understood with regards to the invention as a whole.
A lack of working examples with regards to the claimed invention in context with a “display direction” and “category” and associated undefined elements results in an unclear understanding of the claimed invention.
As a result of the aforementioned analysis, undue experimentation would be required to make the invention based on the contents of the disclosure. For the purposes of examination, a “display direction” and “category” will be interpreted under the broadest reasonable interpretation as a starting orientation and a set of starting orientations, respectively.
Claim 8 recites a “second preset direction”, in which the analysis of enablement of Claim 5 applies.
Claim 9 recites a “second display direction” and “second category”, in which the analysis of enablement of Claim 6 applies.
Claim 16, being similar in scope to Claim 5, is rejected under the same rationale for lack of enablement.
Claim 17, being similar in scope to Claim 6, is rejected under the same rationale for lack of enablement.
Claim 20, being similar in scope to Claim 9, is rejected under the same rationale for lack of enablement.
All dependent claims of claims 5,6, 8, 9, 16, 17, 19, and 20 are similarly rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 6, 8, 9, 16, 17, 19, and 20 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.
Regarding Claim 5, the term “first preset direction” is a relative term which renders the claim indefinite. The term “first preset direction” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Refer to the analysis of “first preset direction” on lack of enablement under 35 U.S.C. 112(a).
Claim 5 recites the limitation "the first preset direction" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding Claim 6, the terms “first display direction” and “first category” are relative terms which renders the claim indefinite. The terms “first display direction” and “first category” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Refer to the analysis of “first display direction” and “first category” on lack of enablement under 35 U.S.C. 112(a).
Regarding Claim 8, the term “second preset direction” is a relative term which renders the claim indefinite. The term “second preset direction” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Refer to the analysis of “second preset direction” on lack of enablement under 35 U.S.C. 112(a).
Claim 8 recites the limitation “the second preset direction” in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding Claim 9, the terms “second display direction” and “second category” are relative terms which renders the claim indefinite. The terms “second display direction” and “second category” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Refer to the analysis of “second display direction” and “second category” on lack of enablement under 35 U.S.C. 112(a).
Claim 16, being similar in scope and structure to Claim 5, is rejected under the same analysis.
Claim 17, being similar in scope and structure to Claim 6, is rejected under the same analysis.
Claim 19, being similar in scope and structure to Claim 8, is rejected under the same analysis.
Claim 20, being similar in scope and structure to Claim 9, is rejected under the same analysis.
All dependent claims of Claims 5, 6, 8, 9, 16, 17, 19, and 20 are similarly rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (WO 2021257094 A1) in view of Chernov (US 20220165388 A1).
Regarding Claim 1, Lee teaches a method for adjusting a model, comprising:
Obtaining a point cloud data of a model in a 3D space (Paragraph [0021]: “The apparatus may orient 102 a model point cloud or a scanned point cloud based on a set of initial orientations. An orientation is a position in a 3D space. For example, an orientation may express a rotation and/or translation of an object model and/or point cloud in 3D space”. Notes: a point cloud is either scanned or implicitly obtained);
Moving the model to a preset position in the 3D space based on the point cloud data (Paragraph [0021]: “The apparatus may orient 102 a model point cloud or a scanned point cloud based on a set of initial orientations. An orientation is a position in a 3D space. For example, an orientation may express a rotation and/or translation of an object model and/or point cloud in 3D space. An initial orientation is a starting orientation of an object model and/or point cloud. For example, a set of initial orientations may include initial orientations for the model point cloud and/or scanned point cloud from which feature determination, correspondence score determination, and/or alignment procedures may be performed. In some examples, orienting 102 a model point cloud or a scanned point cloud based on a set of initial orientations may include orienting (e.g., computing an orientation of) the model point cloud and/or scanned point cloud to an orientation (e.g., rotation and/or translation) indicated by an initial orientation or initial orientations in the set of initial orientations”);
Adjusting the model at the preset position to a first preset orientation for display of the model, based on a neural network model (Paragraph [0024]: “The apparatus may determine 104, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. A portion of a machine learning model is a part of a machine learning model. Examples of portions of a machine learning model may include a layer or layers, a node or nodes, and/or a connection or connections. In some examples, the first portion of the machine learning model may be a portion to determine, extract, and/or encode features of a point cloud or point clouds. For example, the first features may be values (e.g., data, vectors) that represent the model point cloud (e.g., shape, aspects, and/or characteristics of the model point cloud) and/or the second features may be values (e.g., data, vectors) that represent the scanned point cloud (e.g., shape, aspects, and/or characteristics of the scanned point cloud). The first features and/or the second features may be utilized to determine correspondences (e.g., correspondence scores) between the model point cloud and the scanned point cloud. In some examples, the model point cloud (e.g., original model point cloud, normalized model point cloud and/or model point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the first features. In some examples, the scanned point cloud (e.g., original scanned point cloud, normalized scanned point cloud and/or scanned point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the second features”; Paragraph [0025]: “In some examples, the first portion of the machine learning model may be a neural network (e.g., artificial neural network (ANN), CNN, DGCNN, etc.). For instance, the first portion of the machine learning model may include edge convolution layers. In some examples, the first portion of the neural network may include multiple edge convolution layers without a global feature aggregation layer. In some examples, the first portion of the machine learning model may provide and/or indicate features for each point of a point cloud or point clouds (e.g., model point cloud and/or scanned point cloud). Some examples of the first portion of the machine learning model are given herein. Other kinds of machine learning model portions (e.g., neural networks) that operate on point clouds may be used in some examples. In some examples, the first portion of the machine learning model may be referred to as a backbone layer or layers” Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Paragraph [0048]: “In some examples, the apparatus may provide the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison of the alignment model point cloud and the scanned point cloud. For instance, the apparatus may store the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison, may send the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison to another device, and/or may present the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison (on a display and/or in a user interface, for example)”. Notes: The broadest reasonable interpretation of a preset position is a desired orientation of a point cloud. Hence, orienting the point cloud from its initial orientation to the desired orientation is considered adjusting the model to a preset position. Furthermore, Neural networks can be used for feature extraction of the point clouds, which are further used to determine correspondence scores between a point cloud to be adjusted and the point cloud that defines the preset position. The correspondence scores are then utilized to adjust the point cloud to be adjusted using predicted translation and/or rotation matrices).
Lee does not teach obtaining point cloud data of a dental model.
However, Chernov teaches obtaining point cloud data of a dental model in a 3D space (Paragraph [0065]: “The apparatuses and/or methods (e.g., systems, devices, etc.) described below can be used with and/or integrated into an orthodontic treatment plan. The apparatuses and/or methods described herein may be used to segment a patient's teeth from a three-dimensional model, such as a 3D mesh model, a 3D point cloud, or a 3D scan (e.g., CT scan, CBCT scan, MRI scan, etc.)”; Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”).
Lee and Chernov are considered analogous in the art with orientation of models with regards to point clouds of said models. There is clear motivation for the use of point clouds with respect to orientation of dental models, as evidenced by Chernov doing so.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of adjusting the position and orientation of a 3D model with point cloud data using a neural network model of Lee with the motivation of applying point cloud data to orienting dental models of Chernov; Doing so would yield the predictable result of adjusting the position and orientation of a 3D dental model with point cloud data using a neural network model.
Regarding Claim 2, the method of Claim 1 is rejected over Lee as modified.
Lee as modified teaches a first preset orientation being configured to represent at least one of the following:
An orientation of a target tooth in the dental model coincides with an orientation of a first axis in the 3D space (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective” Notes: The dental model (3D point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes),
An orientation of an occlusal surface of the dental model coincides with the orientation of a second axis in the 3D space (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective” Notes: The dental model (3D point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes),
and an orientation of a wide surface of the tooth jaw in the dental model coincides with the orientation of a third axis in the 3D space (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective” Notes: The dental model (3D point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes, and the tooth jaw inherently coincides with the third axis.).
Regarding Claim 3, the method of Claim 2 is rejected over Lee as modified.
Lee teaches adjusting the dental model at the preset position to the first preset orientation based on the neural network model, including:
determining feature vectors of the point cloud data (Lee, Paragraph [0024]: “The apparatus may determine 104, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. A portion of a machine learning model is a part of a machine learning model. Examples of portions of a machine learning model may include a layer or layers, a node or nodes, and/or a connection or connections. In some examples, the first portion of the machine learning model may be a portion to determine, extract, and/or encode features of a point cloud or point clouds. For example, the first features may be values (e.g., data, vectors) that represent the model point cloud (e.g., shape, aspects, and/or characteristics of the model point cloud) and/or the second features may be values (e.g., data, vectors) that represent the scanned point cloud (e.g., shape, aspects, and/or characteristics of the scanned point cloud). The first features and/or the second features may be utilized to determine correspondences (e.g., correspondence scores) between the model point cloud and the scanned point cloud. In some examples, the model point cloud (e.g., original model point cloud, normalized model point cloud and/or model point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the first features. In some examples, the scanned point cloud (e.g., original scanned point cloud, normalized scanned point cloud and/or scanned point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the second features”);
rotating the dental model based on the feature vectors to obtain a rotated dental model (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0043]: “In some examples, the apparatus (e.g., third portion of the machine learning model) may update the first point cloud X with the computed rotation matrix and translation matrix to produce an updated point cloud X.sup.*”);
and adjusting the rotated dental model to the first preset orientation based on the neural network model (Lee, Paragraph [0024]: “The apparatus may determine 104, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. A portion of a machine learning model is a part of a machine learning model. Examples of portions of a machine learning model may include a layer or layers, a node or nodes, and/or a connection or connections. In some examples, the first portion of the machine learning model may be a portion to determine, extract, and/or encode features of a point cloud or point clouds. For example, the first features may be values (e.g., data, vectors) that represent the model point cloud (e.g., shape, aspects, and/or characteristics of the model point cloud) and/or the second features may be values (e.g., data, vectors) that represent the scanned point cloud (e.g., shape, aspects, and/or characteristics of the scanned point cloud). The first features and/or the second features may be utilized to determine correspondences (e.g., correspondence scores) between the model point cloud and the scanned point cloud. In some examples, the model point cloud (e.g., original model point cloud, normalized model point cloud and/or model point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the first features. In some examples, the scanned point cloud (e.g., original scanned point cloud, normalized scanned point cloud and/or scanned point cloud at an initial orientation, etc.) may be input into the first portion of the machine learning model to determine the second features”; Lee, Paragraph [0025]: “In some examples, the first portion of the machine learning model may be a neural network (e.g., artificial neural network (ANN), CNN, DGCNN, etc.). For instance, the first portion of the machine learning model may include edge convolution layers. In some examples, the first portion of the neural network may include multiple edge convolution layers without a global feature aggregation layer. In some examples, the first portion of the machine learning model may provide and/or indicate features for each point of a point cloud or point clouds (e.g., model point cloud and/or scanned point cloud). Some examples of the first portion of the machine learning model are given herein. Other kinds of machine learning model portions (e.g., neural networks) that operate on point clouds may be used in some examples. In some examples, the first portion of the machine learning model may be referred to as a backbone layer or layers”; Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0043]: “In some examples, the apparatus (e.g., third portion of the machine learning model) may update the first point cloud X with the computed rotation matrix and translation matrix to produce an updated point cloud X.sup.*”)
Claim 11, which is similar in scope to Claim 1, is rejected under the same rationale.
Claim 12, which is similar in scope to Claim 1, is rejected under the same rationale.
Claim 13, which is similar in scope to Claim 2, is rejected under the same rationale.
Claim 14, which is similar in scope to Claim 3, is rejected under the same rationale.
Claims 4-9 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (WO 2021257094 A1) in view of Chernov (US 20220165388 A1) and in further view of Kayser (US 20230124868 A1).
Regarding Claim 4, the method of Claim 3 is rejected over Lee as modified.
Lee as modified teaches adjusting the rotated dental model to the first preset orientation based on the neural network model, including:
Using a Dynamic Graph Convolutional Neural Network (DGCNN) model to adjust the rotated dental model to a second preset orientation (Lee, Paragraph [0025]: “In some examples, the first portion of the machine learning model may be a neural network (e.g., artificial neural network (ANN), CNN, DGCNN, etc.). For instance, the first portion of the machine learning model may include edge convolution layers. In some examples, the first portion of the neural network may include multiple edge convolution layers without a global feature aggregation layer. In some examples, the first portion of the machine learning model may provide and/or indicate features for each point of a point cloud or point clouds (e.g., model point cloud and/or scanned point cloud). Some examples of the first portion of the machine learning model are given herein. Other kinds of machine learning model portions (e.g., neural networks) that operate on point clouds may be used in some examples. In some examples, the first portion of the machine learning model may be referred to as a backbone layer or layers”); and
adjusting the second preset orientation of the rotated dental model to the first preset orientation (Lee, Paragraph [0046]: “In some examples, the apparatus may refine a global alignment. For example, the apparatus may align the model point cloud and the scanned point cloud based on the global alignment at a finer scale than the global alignment. For instance, the apparatus may align the model point cloud and the scanned point cloud using an iterative closest point (ICP) technique. In some examples, if the 3D object model is represented as a mesh (in addition to the model point cloud, for instance), the apparatus may utilize a plane-to-plane ICP approach. In some examples, if the 3D object model is not represented as a mesh, the apparatus may utilize a point-to-plane ICP approach. An ICP technique may be utilized to determine a closed form solution of rotation and translation matrices. In some examples, rotation and translation matrices may be determined in accordance with Equation (6). In some examples, the apparatus may utilize the ICP technique to refine the correspondences between the model point cloud and the scanned point cloud with respect to a fixed rotation matrix R.sub.xy and translation matrix T.sub.xy” Notes: Lee teaches a second alignment of the point clouds in a refining stage, which utilizes the ICP algorithm).
Lee as modified does not teach using a residual neural network model to adjust the second preset orientation to adjust the rotated dental model to the first preset orientation.
However, Kayser teaches using a residual neural network model to adjust the preset orientation to adjust the model to the another orientation (Paragraph [0038]: “In one embodiment, the encoder 10 extracts characteristics or features from the partial point cloud 2 and estimates the transformation in the embedding space 30. As already mentioned above, in one embodiment, SALs based on PointNet++ and a ResNet-based PointNet are used to process the point cloud (see FIG. 2). By using SALs, local characteristics can be extracted with three different radii and propagated to one or more further layers in order to obtain a global characteristic. As already described, the global characteristic can be attached to all points of the point cloud and entered into the ResNet-based PointNet in order to estimate the transformation embedding 30. The use of both local and global characteristics allows for a particularly accurate estimation of the orientation of the object”; Paragraph [0041]: “In some embodiments of the disclosure, loss functions may be used as described below. As already described above, in one embodiment, the decoder 20 obtains a concatenated vector having a 3D point and embedding vector, in order to estimate SDF values and associated 3D coordinates in the canonical space. In this case, the loss functions of shape loss, L.sub.S, and correspondence loss, L.sub.C, can be formulated in order to estimate accurate SDF values and correspondences to the canonical orientation”; Paragraph [0042]: “With respect to the encoder 10, the partial point cloud 2 of the n-th object, transformed using the k-th rotation, P.sub.n.sup.k, and a one-hot vector, OH.sub.n, are used as input for the encoder 10 in order to estimate transformation embedding, T.sub.k. The transformation embedding along with the object embedding, O.sub.n, are concatenated with each point of the point cloud, X.sub.n.sup.k, and are used as input for the decoder in order to estimate the SDF values, S.sub.n.sup.k, and the 3D point correspondences”; Refer to Figure 1 to supplement the aforementioned passages. Notes: ResNet is shorthand for a residual neural network. Kayser teaches using Resnet to process a point cloud for accurate estimation of the orientation of the object, where Resnet is used to estimate transformation embeddings 30, which can be seen in Kayser, Figure 1. Kayser also teaches obtaining 3D point correspondences from transformation embeddings, which are concatenated with each point in the point cloud to determine 3D point correspondences. As taught by Lee as modified, 3D correspondence values can be used to compute rotation and translation matrices to update the point cloud to match a desired orientation).
Lee as modified and Kayser are considered analogous in the art with regards to the orientation of point clouds using neural networks. A common motivation in the art is efficient use of resources with regards to machine learning. Resnet is a computationally expensive variant of a neural network; hence, in the context of the art, there would be a motivation to use Resnet when the orientation of a point cloud needs to be finetuned, as opposed to a more drastic orientation task. Using Resnet over another neural network would be appropriate for such a task.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the orientation method of a 3D model of Lee as modified with the use of Resnet for adjusting the orientation of a 3D model of Kayser; Doing so would yield the predictable result of a 2-step 3D model orientation process, in which the second step would be a fine tuning process after an initial adjustment in the first step.
Regarding Claim 5, the method of Claim 4 is rejected over Lee as modified.
Lee as modified teaches adjusting the rotated dental model to the second preset orientation by using the DGCNN model, including:
Using the DGCNN model to determine the first preset direction of the rotated dental model in the 3D space (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of preset direction will be rotation/translation matrices, which are computed from correspondence scores of the point clouds); and
Adjusting the rotated dental model to the second preset orientation by using the first preset direction (Lee, Paragraph [0043]: “In some examples, the apparatus (e.g., third portion of the machine learning model) may update the first point cloud X with the computed rotation matrix and translation matrix to produce an updated point cloud X.sup.*”. Notes: The broadest reasonable interpretation of preset direction will be transformation/translation matrices, which are computed from correspondence scores of the point clouds).
Regarding Claim 6, the method of Claim 5 is rejected over Lee as modified.
Lee as modified teaches determining the first preset direction of the rotated dental model in the 3D space by using the DGCNN model, including:
Identifying a first display direction of the rotated dental model in the 3D space by using the DGCNN model (Lee, Paragraph [0025]: “In some examples, the first portion of the machine learning model may be a neural network (e.g., artificial neural network (ANN), CNN, DGCNN, etc.). For instance, the first portion of the machine learning model may include edge convolution layers. In some examples, the first portion of the neural network may include multiple edge convolution layers without a global feature aggregation layer. In some examples, the first portion of the machine learning model may provide and/or indicate features for each point of a point cloud or point clouds (e.g., model point cloud and/or scanned point cloud). Some examples of the first portion of the machine learning model are given herein. Other kinds of machine learning model portions (e.g., neural networks) that operate on point clouds may be used in some examples. In some examples, the first portion of the machine learning model may be referred to as a backbone layer or layers”; Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”);
Classifying the first display direction to obtain a first category to which the first display direction belongs (“Lee, Paragraph [0025]: “In some examples, the first portion of the machine learning model may be a neural network (e.g., artificial neural network (ANN), CNN, DGCNN, etc.). For instance, the first portion of the machine learning model may include edge convolution layers. In some examples, the first portion of the neural network may include multiple edge convolution layers without a global feature aggregation layer. In some examples, the first portion of the machine learning model may provide and/or indicate features for each point of a point cloud or point clouds (e.g., model point cloud and/or scanned point cloud). Some examples of the first portion of the machine learning model are given herein. Other kinds of machine learning model portions (e.g., neural networks) that operate on point clouds may be used in some examples. In some examples, the first portion of the machine learning model may be referred to as a backbone layer or layers”; Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of a “first category” is considered akin to first display direction, or perhaps the general starting orientation. In this case, each possible starting orientation can be considered its own category in the broadest reasonable interpretation, and hence, the first category is the same as the first display direction (starting direction); furthermore, Lee teaches generating transformation and rotation matrices for an initial starting orientation to a desired orientation state); and
Determining the first preset direction corresponding to the first category by using a first preset correspondence (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of preset direction will be rotation/translation matrices, which are computed from correspondence scores of the point clouds)
Wherein the first preset correspondence is configured to represent the correspondence between the first category and the first preset direction (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of preset direction will be rotation/translation matrices, which are computed from correspondence scores of the point clouds, where correspondence scores are used to calculate the translation and rotation matrices).
Regarding Claim 7, the method of Claim 4 is rejected over Lee as modified.
Lee teaches configuring the second preset orientation to represent at least one of the following:
The orientation of the target tooth in the rotated dental model coincides with the orientation of the first axis (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective”. Notes: The dental model (3d point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes),
The orientation of the occlusal surface of the target tooth in the rotated dental model coincides with a target plane (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective”. Notes: The dental model (3d point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes),
And the target plane is constructed from the first axis and the third axis (Chernov, Paragraph [0119]: “The transformation engine 188 may implement one or more automated agents configured to adjust the position and orientation of the generic tooth model to better match the position and orientation of the selected segmented tooth from the 3D scan data”; Chernov, Paragraph [0066]: “The three-dimensional scan can generate a 3D mesh model, or a 3D point cloud model representing the patient's arch”; Chernov, Paragraph [0071]: “The scanning system 154 may include a computer system configured to scan a patient's dental arch. A “dental arch,” as used herein, may include at least a portion of a patient's dentition formed by the patient's maxillary and/or mandibular teeth, when viewed from an occlusal perspective”. Notes: The dental model (3d point cloud model) is scanned from the occlusal position, which inherently orients the model with respect to all 3 axis such that the occlusal plane aligns with 2 of the three planes).
Regarding Claim 8, the method Claim 4 is rejected over Lee as modified.
Lee teaches adjusting the second preset orientation by using the residual neural network model to adjust the rotated dental model to the first preset orientation, including:
Determining the second preset direction of the rotated dental model in the 3D space by using the residual neural network model (Kayser, Fig 1; Kayser, Paragraph [0038]: “In one embodiment, the encoder 10 extracts characteristics or features from the partial point cloud 2 and estimates the transformation in the embedding space 30. As already mentioned above, in one embodiment, SALs based on PointNet++ and a ResNet-based PointNet are used to process the point cloud (see FIG. 2). By using SALs, local characteristics can be extracted with three different radii and propagated to one or more further layers in order to obtain a global characteristic. As already described, the global characteristic can be attached to all points of the point cloud and entered into the ResNet-based PointNet in order to estimate the transformation embedding 30. The use of both local and global characteristics allows for a particularly accurate estimation of the orientation of the object”; Kayser, Paragraph [0041]: “In some embodiments of the disclosure, loss functions may be used as described below. As already described above, in one embodiment, the decoder 20 obtains a concatenated vector having a 3D point and embedding vector, in order to estimate SDF values and associated 3D coordinates in the canonical space. In this case, the loss functions of shape loss, L.sub.S, and correspondence loss, L.sub.C, can be formulated in order to estimate accurate SDF values and correspondences to the canonical orientation”; Kayser, Paragraph [0042]: “With respect to the encoder 10, the partial point cloud 2 of the n-th object, transformed using the k-th rotation, P.sub.n.sup.k, and a one-hot vector, OH.sub.n, are used as input for the encoder 10 in order to estimate transformation embedding, T.sub.k. The transformation embedding along with the object embedding, O.sub.n, are concatenated with each point of the point cloud, X.sub.n.sup.k, and are used as input for the decoder in order to estimate the SDF values, S.sub.n.sup.k, and the 3D point correspondences”; Refer to Figure 1 to supplement the aforementioned passages. Notes: ResNet is shorthand for a residual neural network. Kayser teaches using Resnet to process a point cloud for accurate estimation of the orientation of the object, where Resnet is used to estimate transformation embeddings 30, which can be seen in Kayser, Figure 1. Kayser also teaches obtaining 3D point correspondences from transformation embeddings, which are concatenated with each point in the point cloud to determine 3D point correspondences. As taught by Lee as modified, 3D correspondence values can be used to compute rotation and translation matrices to update the point cloud to match a desired orientation);
And adjusting the second preset orientation by using the second preset direction to adjust the rotated dental model to the first preset orientation (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6); Lee, Paragraph [0043]: “In some examples, the apparatus (e.g., third portion of the machine learning model) may update the first point cloud X with the computed rotation matrix and translation matrix to produce an updated point cloud X.sup.*)”).
Regarding Claim 9, the method of Claim 8 is rejected over Lee as modified.
Lee as modified teaches determining the second preset direction of the rotated dental model in the 3D space by using the residual neural network model, including:
Identifying a second display direction of the rotated model in the 3D space by using the residual neural network model (Kayser, Fig 1; Kayser, Paragraph [0038]: “In one embodiment, the encoder 10 extracts characteristics or features from the partial point cloud 2 and estimates the transformation in the embedding space 30. As already mentioned above, in one embodiment, SALs based on PointNet++ and a ResNet-based PointNet are used to process the point cloud (see FIG. 2). By using SALs, local characteristics can be extracted with three different radii and propagated to one or more further layers in order to obtain a global characteristic. As already described, the global characteristic can be attached to all points of the point cloud and entered into the ResNet-based PointNet in order to estimate the transformation embedding 30. The use of both local and global characteristics allows for a particularly accurate estimation of the orientation of the object”; Kayser, Paragraph [0041]: “In some embodiments of the disclosure, loss functions may be used as described below. As already described above, in one embodiment, the decoder 20 obtains a concatenated vector having a 3D point and embedding vector, in order to estimate SDF values and associated 3D coordinates in the canonical space. In this case, the loss functions of shape loss, L.sub.S, and correspondence loss, L.sub.C, can be formulated in order to estimate accurate SDF values and correspondences to the canonical orientation”; Kayser, Paragraph [0042]: “With respect to the encoder 10, the partial point cloud 2 of the n-th object, transformed using the k-th rotation, P.sub.n.sup.k, and a one-hot vector, OH.sub.n, are used as input for the encoder 10 in order to estimate transformation embedding, T.sub.k. The transformation embedding along with the object embedding, O.sub.n, are concatenated with each point of the point cloud, X.sub.n.sup.k, and are used as input for the decoder in order to estimate the SDF values, S.sub.n.sup.k, and the 3D point correspondences”; Lee, Paragraph [0043]: “In some examples, the apparatus (e.g., third portion of the machine learning model) may update the first point cloud X with the computed rotation matrix and translation matrix to produce an updated point cloud X.sup.*”; Refer to Figure 1 to supplement the aforementioned passages. Notes: ResNet is shorthand for a residual neural network. Kayser teaches using Resnet to process a point cloud for accurate estimation of the orientation of the object, where Resnet is used to estimate transformation embeddings 30, which can be seen in Kayser, Figure 1. Kayser also teaches obtaining 3D point correspondences from transformation embeddings, which are concatenated with each point in the point cloud to determine 3D point correspondences. As taught by Lee as modified, 3D correspondence values can be used to compute rotation and translation matrices to update the point cloud to match a desired orientation. A second display direction is taken in BRI to mean the orientation after first translation/rotation);
Classifying the second display direction to obtain a second category to which the second display direction belongs; and determining the second preset direction corresponding to the second category by using a second preset correspondence (Kayser, Fig 1; Kayser, Paragraph [0038]: “In one embodiment, the encoder 10 extracts characteristics or features from the partial point cloud 2 and estimates the transformation in the embedding space 30. As already mentioned above, in one embodiment, SALs based on PointNet++ and a ResNet-based PointNet are used to process the point cloud (see FIG. 2). By using SALs, local characteristics can be extracted with three different radii and propagated to one or more further layers in order to obtain a global characteristic. As already described, the global characteristic can be attached to all points of the point cloud and entered into the ResNet-based PointNet in order to estimate the transformation embedding 30. The use of both local and global characteristics allows for a particularly accurate estimation of the orientation of the object”; Kayser, Paragraph [0041]: “In some embodiments of the disclosure, loss functions may be used as described below. As already described above, in one embodiment, the decoder 20 obtains a concatenated vector having a 3D point and embedding vector, in order to estimate SDF values and associated 3D coordinates in the canonical space. In this case, the loss functions of shape loss, L.sub.S, and correspondence loss, L.sub.C, can be formulated in order to estimate accurate SDF values and correspondences to the canonical orientation”; Kayser, Paragraph [0042]: “With respect to the encoder 10, the partial point cloud 2 of the n-th object, transformed using the k-th rotation, P.sub.n.sup.k, and a one-hot vector, OH.sub.n, are used as input for the encoder 10 in order to estimate transformation embedding, T.sub.k. The transformation embedding along with the object embedding, O.sub.n, are concatenated with each point of the point cloud, X.sub.n.sup.k, and are used as input for the decoder in order to estimate the SDF values, S.sub.n.sup.k, and the 3D point correspondences”; Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of a “second category” is considered akin to second display direction, or perhaps the general orientation after a first translation or rotation. In this case, each possible orientation can be considered its own category in the broadest reasonable interpretation, and hence, the first category is the same as the first display direction (starting direction); furthermore, Lee teaches generating transformation and rotation matrices for an initial orientation to a desired orientation state);
Wherein the second preset correspondence is configured to represent the correspondence between the second category and the second preset direction (Lee, Paragraph [0041]: “The apparatus may globally align 108 the model point cloud and the scanned point cloud based on the correspondence scores. For example, the apparatus may globally align the model point cloud to the scanned point cloud or may globally align the scanned point cloud to the model point cloud based on the correspondence scores. In some examples, the apparatus may use a third portion of the machine learning model to globally align 108 the model point cloud and the scanned point cloud. For instance, the third portion of the machine learning model may infer and/or predict a rotation matrix and/or translation matrix to align the model point cloud and the scanned point cloud based on the correspondence scores”; Lee, Paragraph [0042]: “In some examples, the second portion of the machine learning model may provide the correspondence scores (e.g., probabilities) for each point in a first point cloud (e.g., model point cloud or scanned point cloud) to a second point cloud (e.g., scanned point cloud or model point cloud). For instance, the correspondence scores may indicate correspondence probabilities of point pairs between the model point cloud and the scanned point cloud. The apparatus (e.g., third portion of the machine learning model) may determine point cloud pairs (X, Y), where X = { X.sub.lt ... , X.sub.n } are the points of the first point cloud and Y = {y.sub.t, ...,y.sub.n} are the corresponding points of the second point cloud. From the point cloud pairs, the apparatus (e.g., third portion of the machine learning model) may compute a closed form solution of the rotation matrix R.sub.xy and translation matrix T.sub.xy from the first point cloud X to the second point cloud Y. For example, the apparatus may compute the rotation matrix and translation matrix using a singular value decomposition of a covariance matrix H = orthogonal matrix, S is a diagonal matrix, V is an orthogonal matrix, T denotes transpose, and X and y are centroids of X and Y, respectively. Examples of the rotation and translation matrices are given in Equation (6)”. Notes: The broadest reasonable interpretation of preset direction will be rotation/translation matrices, which are computed from correspondence scores of the point clouds, where correspondence scores are used to calculate the translation and rotation matrices).
Claim 15, which is similar in scope to Claim 4, is rejected under the same rationale.
Claim 16, which is similar in scope to Claim 5, is rejected under the same rationale.
Claim 17, which is similar in scope to Claim 6, is rejected under the same rationale.
Claim 18, which is similar in scope to Claim 7, is rejected under the same rationale.
Claim 19, which is similar in scope to Claim 8, is rejected under the same rationale.
Claim 20, which is similar in scope to Claim 9, is rejected under the same rationale.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (WO 2021257094 A1) in view of Chernov (US 20220165388 A1) and Kayser (US 20230124868 A1), in further view of Bogacz (WO 2023086756 A1).
Regarding Claim 10, the method of Claim 1 is rejected over Lee as modified.
Lee as modified teaches moving the dental model to the preset position in the 3D space based on the point cloud data (Lee, Paragraph [0021]: “The apparatus may orient 102 a model point cloud or a scanned point cloud based on a set of initial orientations. An orientation is a position in a 3D space. For example, an orientation may express a rotation and/or translation of an object model and/or point cloud in 3D space. An initial orientation is a starting orientation of an object model and/or point cloud. For example, a set of initial orientations may include initial orientations for the model point cloud and/or scanned point cloud from which feature determination, correspondence score determination, and/or alignment procedures may be performed. In some examples, orienting 102 a model point cloud or a scanned point cloud based on a set of initial orientations may include orienting (e.g., computing an orientation of) the model point cloud and/or scanned point cloud to an orientation (e.g., rotation and/or translation) indicated by an initial orientation or initial orientations in the set of initial orientations”).
Lee as modified does not teach determining a middle position of the point cloud data, determining a coordinate origin of the dental model in the 3D space based on the middle position, and moving the dental model to the preset position based on the coordinate origin.
However, moving a model to a preset position based on the coordinate origin is considered obvious in the art, and is inherent to 3D modeling and 3D modeling software.
Furthermore, Bogacz teaches determining a middle position of the point cloud data (Paragraph [0038]: “Each level of integrity verification may be associated with a different data selection algorithm, methodology, or technique for ensuring the integrity of a different amount or sampling of data from the fde. As shown in FIG. 2, CGVS 100 may determine (at 204) a particular level of integrity verification for the received point cloud file that is associated with selecting (at 206) data points within various planes defined from a center point of the point cloud. Specifically, CGVS 100 may identify the center point within the 3D space represented by the point cloud”);
Determining a coordinate origin of the model in the 3D space based on the middle position (Paragraph [0024]: “Each point cloud data point may include positional and non-positional values. The positional values may include coordinates within 3D space. For instance, each point cloud data point may include x-coordinate, y-coordinate, and z-coordinate data point values for each imaged point, feature, element, object of the 3D environment”; Notes: while Bogacz does not explicitly teach a dental model, the teaching of Bogacz can be applied to any model and associated point cloud data. In its broadest reasonable interpretation, a coordinate origin (x,y,z=0) is inherent to a 3D space. Given that the preset position is given (which can be a desired orientation, which itself can have a middle position with xyz coordinates) and the original orientation has a middle position with xyz coordinates, moving the dental model to the preset position given both coordinates is inherent to modeling in 3D spaces).
Lee as modified and Bogacz are considered analogous in the art with respect to working with 3D models and associated point cloud data. Visually centering models for viewing is common place in the art; a motivation for using a middle position of point cloud data of a model to determining a coordinate origin of the dental model would be to more consistently center the 3D model for viewing.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of moving the dental model in a 3D space based on point cloud data of Lee as modified with the method of determining a middle point of point cloud data and its associated coordinate origin of a 3D model; doing so would yield the predictable result of a method for consistently moving a dental model in a 3D space through a central point in its point cloud data, and establishing an axis in relation to the central point.
Response to Arguments
Applicant's arguments filed June 9th, 2026 have been fully considered but they are not persuasive.
The rejections of Claims 5, 6, 8, 9, 16, 17 and 20 under 35 U.S.C. 112(a) are maintained.
Regarding Claim 5, Applicant asserts that the present application expressly defines the “the first preset direction” in Paragraph [0094] (“The aforementioned first preset direction may be the preset direction. By identifying the point cloud data of the rotated dental model, the first preset direction corresponding to the rotated dental model may be obtained”). Notably, a first preset direction is defined as being the preset direction, which itself is not concretely defined. Beyond the vague definition put forth in the Paragraph [0094], the preset direction is also defined in relation to other elements, most notably in Paragraph [0011], which defines it as “corresponding to the first category by using the preset correspondence”. Both “category and correspondence” are similarly lacking clear definition with regards to the claimed invention. Furthermore, while Paragraph [0095] describes a numerical example for a first preset direction (Paragraph [0095]: “for example, if the first preset direction is 20
°
and the second preset orientation is 40
°
, and based on the preset rotation relationship, that is, rotating the first preset direction by 20
°
, the rotated dental model in the first preset direction may be adjusted to the second preset orientation”), the Examiner notes that simply referring to direction and orientation in terms of degrees is insufficient to describe a rotation basis or orientation basis for 3D orientation, to which the Applicant’s invention pertains to, as supported in Paragraph [0095]: “The DGCNN model may be configured to recognize the rotated dental model, determine the first preset direction of the rotated dental model in the 3D space, and adjust the rotated dental model to the second preset orientation based on the first preset direction of the rotated dental model in the 3D space” and as described in Figure 12. A single value in degrees is an inadequate measure of describing 3D orientations of models, as degrees describe a direction with respect to a plane defined by 2 axis. While a preset direction is related to an associated value in degrees, Simply stating the value in degrees as a preset direction is insufficient, since there is no elaboration on the vector direction of the preset direction. While it can be argued that the preset direction is towards a specific orientation, the orientation itself similarly vaguely defined within the specifications. Although the broadest reasonable interpretation of orientation is more obvious, the definition and example of a preset direction is only clear when the orientation is defined in terms of degrees, which while apparent in Paragraph [0095], is insufficient for describing orientation of a 3D model in isolation, as stated previously). Therefore, while the Applicant provides a numerical example, the example is not considered a working example as result of a lack of clear definition and separation between a preset direction and preset orientation.
Regarding Claim 6, the Applicant asserts that the present application expressly defines “the first display direction” in Paragraph [0097] (“the aforementioned first display direction may be the actual display orientation of the rotated dental model in the 3D space”). However, orientation is not concretely defined within the specification. Although the broadest reasonable interpretation of orientation is more obvious, the definition and example of a direction is only clear when the orientation is defined in terms of degrees, which while apparent in Paragraph [0095], is insufficient for describing orientation of a 3D model in isolation, as stated previously. Furthermore, the distinction between direction and orientation is unclear in view of the example of Paragraph [0095], and in the aforementioned passage of Paragraph [0097]). The applicant further asserts that the display direction is a directional vector in 3D space representing how the dental model is currently oriented relative to the coordinate system. The Examiner notes that such an interpretation further degenerates the distinction between a “direction” and “an orientation”. Furthermore, as noted in the analysis of Claim 5, although the broadest reasonable interpretation of orientation is more obvious, the definition and example of a direction is only clear when the orientation is defined in terms of degrees, which while apparent in Paragraph [0095], is insufficient for describing orientation of a 3D model in isolation, as stated previously). Therefore, while the Applicant provides a numerical example, the example is not considered a working example as result of a lack of clear definition and separation between a preset direction and preset orientation.
The Applicant relies on Figure 12 as a visual depiction of 3D orientation space partitioned into categories pertaining to a “first preset category” and a “second preset category”. While Figure 12 does visualize the directions as falling into categories, and provide an example of the categories, where categories are ultimately defined by a set of boundary directions, it is unclear how the presented definition and examples of direction and orientation relate to the distinct categories. In particular, the lack of clear definition and distinction of direction and orientation, in combination with the fact that values in degrees in isolation are insufficient to teach direction and orientation in a 3D environment, would make it difficult for a person having ordinary skill in the art to understand a preset category, which contains the directions/orientations. To summarize, while a category is defined in context with direction/orientation, the lack of clear and sufficient definition and distinction of direction and orientation result in the definition of a category as a whole to be unclear, and prevents clear understanding of Figure 12 as a working example.
Regarding Claims 8 and 9, the applicant defines preset directions, display directions and categories in vague terminology that is not sufficiently supported by working examples. Refer to the previous analysis of Claims 5 and 6 as well as the Rejections section regarding the unclear definition of preset directions, display directions and categories.
Claims 16, 17 and 20, being similar in scope to Claims 5, 6, and 9 respectively, are considered under the same analysis of Claims 5, 6, and 9.
The Examiner notes that Claims 5, 6, 8, 9, 16, 17, 19, and 20 have not been amended to clarify disputed terms.
The rejections of Claims 5, 6, 8, 9, 16, 17, 19 and 20 under 35 U.S.C. 112(b) are maintained.
Refer to the Examiner response to Arguments regarding 35 U.S.C. 112(a) and the Rejection section.
The Examiner notes that Claims 5, 6, 8, 9, 16, 17, 19, and 20 have not been amended to clarify disputed terms.
The rejections of Claims 1-20 under 35 U.S.C. 103 are maintained.
Regarding Claims 1-3, the Applicant asserts that Lee does not qualify as prior art because it is not analogous to the present invention as described in the presently pending claims.
The Examiner asserts that the characterization of Lee and the claimed invention being analogous with respect to orientation of models with respect to point clouds of said models is not overly broad. A person having ordinary skill in the art would consider Lee and the claimed invention to be analogous in the art in the sense that Lee teaches orienting a point cloud to a desired orientation, which is the main subject of the claimed invention.
The Applicant further asserts that the field of endeavor differs between Lee and the claimed invention. However, both Lee and the claimed invention are methods seeking to display a model after reorientation through an associated model point cloud. While Lee does not explicitly teach doing so for a dental model, Chernov demonstrates that the use of 3D point clouds in conjunction with 3D models is well known. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention that the reorientation of models for higher quality in display can be applied to dental models.
In response to Applicant's argument that the references differ from the claimed invention with respect to orientation using a single point cloud as opposed to multiple point clouds, the claim language does not indicate that a point cloud be oriented to a desired orientation with solely the point cloud itself.
In response to the Applicant’s argument that Lee does not consider single point cloud orientation, Lee aligns a first point cloud to another point cloud (see Rejection of Claim 1). As mentioned previously, the claim language does not indicate that a point cloud be oriented to a desired orientation with solely the point cloud itself. Lee also displays the orientation of a point cloud model to a desired orientation (second point cloud model orientation); see the Rejection of Claim 1. The Applicant contends that Lee’s field of endeavor excludes the claimed invention’s field. However, as noted previously, Lee and the claimed invention share the same field of endeavor, being the orientation of a point cloud model to a desired orientation.
In response to the Applicant’s argument that Lee and the claimed invention do not target the same problem, the Examiner asserts that Lee addresses aligning a model point cloud to a desired orientation described by a second point cloud. Whether or not the problem addressed by a reference is exactly the same is not relevant to whether a person having ordinary skill in the art would have consulted the reference when faced with the problem that the inventor was trying to solve. Given that the claimed invention deals with orienting a point cloud, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to consult methods of orienting a point cloud irrespective to the use of another point cloud; the Applicant specification does not expressly indicate directly or indirectly that the orientation of a point cloud without the use of another point cloud as being a targeted problem or motivation.
Furthermore, registration and display orientation are not far removed, because registration deals with the translation and rotation of a point cloud to align with another point cloud. As noted by Lee in Paragraph [0048] (“In some examples, the apparatus may provide the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison of the alignment model point cloud and the scanned point cloud. For instance, the apparatus may store the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison, may send the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison to another device, and/or may present the global alignment, refined alignment, aligned model point cloud and scanned point cloud, and/or the comparison (on a display and/or in a user interface, for example)”), the display of an orientation of the aligned point cloud is clearly related to registration. The broadest reasonable interpretation of display-orientation of a point cloud is the orientation of a point cloud for display, which Lee demonstrates. Therefore, Lee is reasonably pertinent to the problem faced by the inventor.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Chernov teaches obtaining point cloud data of a dental model in a 3D space, which is utilized in combination with primary reference Lee to teach Claim 1 as a whole. Refer to the rejection of Claim 1 for more analysis and explanations.
Hence, Claim 1 is not patentable over Lee and Chernov, and Claims 2 and 3 are subsequently not patentable for the sole reason of the patentability of Claim 1.
Regarding Claim 11 and 12, the inclusion of the limitation “for display of the dental model” is taught by Lee, in combination with Chernov. Refer to the Rejection of Claim 1, where Claim 11 and 12 are rejected under the same rationale as Claim 1.
Regarding Claim 13, refer to the rejection of Claim 2 in the Rejections, in which Claim 13 is similarly rejected under the same rationale.
Regarding Claim 14, refer to the rejection of Claim 3 in the Rejections, in which Claim 14 is similarly rejected under the same rationale.
Regarding Claims 4-9, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Kayser teaches using a residual neural network model to adjust the preset orientation to adjust the model to the another orientation (Refer to the Rejection of Claim 4).
The Examiner notes that Kayser is related to Lee, as well as the claimed invention, in the orientation of point clouds using neural networks, where Lee has been combined with Chernov to teach its relevance with dental models (Refer to the Rejections section for more analysis and explanation).
With regards to “for display of the dental model”, refer to the rejection of Claim 1, where Lee teaches displaying a reoriented model through a point cloud, and Chernov teaches that point clouds are commonly utilized in dental models.
Therefore, Claims 4-9, being dependent on amended Claim 1, are not patentable over Lee, Chernov, and Kayser for at least the same reasons.
Regarding Claim 15, refer to the rejection of Claim 4 in the Rejections, in which Claim 15 is similarly rejected under the same rationale.
Regarding Claim 16, refer to the rejection of Claim 5 in the Rejections, in which Claim 16 is similarly rejected under the same rationale.
Regarding Claim 17, refer to the rejection of Claim 6 in the Rejections, in which Claim 17 is similarly rejected under the same rationale.
Regarding Claim 18, refer to the rejection of Claim 7 in the Rejections, in which Claim 18 is similarly rejected under the same rationale.
Regarding Claim 19, refer to the rejection of Claim 8 in the Rejections, in which Claim 19 is similarly rejected under the same rationale.
Regarding Claim 20, refer to the rejection of Claim 9 in the Rejections, in which Claim 13 is similarly rejected under the same rationale.
Regarding Claim 10, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Bogacz teaches determining a middle position of the point cloud data and determining a coordinate origin of the model in the 3D space based on the middle position, where moving a model to a preset position based on the coordinate origin is considered obvious in the art, and is inherent to 3D modeling and 3D modeling software (Refer to the Rejection of Claim 10 for more analysis and explanation, as well as reason and justification for combination)
With regards to “for display of the dental model”, refer to the rejection of Claim 1, where Lee teaches displaying a reoriented model through a point cloud, and Chernov teaches that point clouds are commonly utilized in dental models.
Conclusion
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 RAYMOND CHUN LAM LI whose telephone number is (571)272-5124. The examiner can normally be reached M-F 8:30-5.
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/RAYMOND CHUN LAM LI/Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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