DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-15 and 17-20 are presented for examination based on the amended claims in the application filed on February 5, 2026. Claim 16 has been cancelled by the applicant.
Claims 1-15 and 17-20 are rejected under 35 USC § 101 because the claimed invention is directed to judicial exception, an abstract idea, and it has not been integrated into practical application. The claims further do not recite significantly more than the judicial exception.
Claims 1, 9-11, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0164055 A1 Ljung Larhed, et al. in view of Heimann, et a l. “3D Active Shape Models Using Gradient Descent Optimization of Description Length” Information Processing in Medical Imaging, pp. 566-577 (July 2005).
Claims 2-3, 5, 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claims 1 and 11 above, and further in view of Bronstein et al. “Geometric Deep Learning: Going beyond Euclidean data” IEEE Signal Processing Magazine Volume: 34, Issue: 4 (July 2017).
Claims 4, 6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed, Heimann, and Bronstein as applied to claims 2 and 12 above, and further in view of US Patent 9,922,432 B1 Risser.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claim 1, and further in view of US 2007/0055401 A1 Van Bael et al.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claim 11 above, and further in view of US Patent 9,922,432 B1 Risser.
This action is made Final.
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 February 5, 2026 has been entered. Claims 1-15 and 17-20 remain pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 17-20 are rejected under 35 USC § 101 because the claimed invention is directed to judicial exception, an abstract idea, and it has not been integrated into practical application. The claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-10 are directed to a method and fall within the statutory category of process; Claims 11-15 and 17-19 are directed to computer readable media and fall within the statutory category of articles of manufacture; and Claim 20 is directed to a system and falls within the statutory category of machine. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry, “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?”, we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 1, 11, and 20: The limitations of:
“generating a first plurality of style signals based on a first three dimensional (3D) computer-aided design (CAD) object”,
“generating a second plurality of style signals based on a second 3D CAD object”,
“computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object, wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position”,
“generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient”, and
“positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object to generate a first visualization of the first geometric style gradient”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, the limitations can be conducted as the following:
a person can mentally create or draw with a pen and paper style information of a first 3D object indicating a certain object style such as size and shape,
a person can mentally create style information of a second 3D object indicating a certain object style,
a person can mentally or draw with a pen and paper, after generating the style information on the objects, calculate a different partial derivation of the style metrics data with respect to positions corresponding to the 3D CAD object using nothing more mathematic calculus formulas being different partial derivatives to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable (i.e., the positions),
a person can mentally create or draw with a pen and paper a graphic element on a canvas using a direction vector along with coordinates and geometric information of the gradient, and
a person can mentally position or draw with a pen and paper the graphical element on the canvas with the first 3D object on the position to match the gradient position.
Furthermore, Para. 0058 teaches, “the style signal set provides a representation of stylistic aspects of the 3D CAD object at different scales,” and para. 0038 teaches, “the style comparison metric 170 can be any type of measurement technique that can be used to quantify the similarity or dissimilarity in geometric style between two 3D CAD objects or a ‘pair of 3D CAD objects.’” Therefore, a person can extract stylistic aspects of the 3D object such has its curvature, orientation, or size and use these aspects [style signals] in comparing its similarity to other 3D objects.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, claims 1, 11, and 20: The limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object, wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position”, as drafted, is an process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, after generating the style information, calculating a different partial derivation of the style metrics data with respect its positioning on the first 3D CAD object using nothing more mathematic calculus formulas. Different partial derivatives are known mathematical evaluations to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable (i.e., the positions) (see Para. 0043 and 0017-0019, Para. 0043 teaches, “the style comparison application 180 can perform any number and/or types of ranking operations, statistical operations, filtering operations, any other type of mathematical operations, plotting operations, any other type of graphical operations, or any combination thereof on any number of the metric values 440 and optionally any number of previously generated metric values to generate any number and/or types of evaluation results”, Para. 0117 teaches, “the partial derivative engine 520 defines the style gradient 540(1) as a set of vectors representing a different partial derivatives of the style comparison metric 170 with respect to each 3D position included in the sample set 328(1),” and Equations 7a and 7b shows the style gradient calculation being a function of the partial derivatives of the metric values (Dstyle) and positions (cxyz and sxyz). Therefore, the partial derivatives which are mathematical concepts can be used to produce gradients with respect to positions of 3D CAD objects.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic operation but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, yes, claims 1, 11, and 20 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1, 11, and 20: The judicial exception is not integrated into a practical application. In particular, the claim 20 recites the following additional elements: “via execution of a trained neural network”, “within a graphical user interface”, “One or more non-transitory computer readable media including instructions that, when executed by one or more processors”, and “A system comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories”, as drafted, which are merely recitations of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broad reasonable interpretation, which does not integrate a judicial exception into elements.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application?” No, the claim does not integrate the abstract idea into a practical application and does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 11, and 20 does not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Claim 1, 11, and 20: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 11, and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 2 and 12, they recite an additional element recitation of “executing the trained neural network at least once to map the first 3D CAD object to a first feature map set” which is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, these claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, these claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as they have not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, claims 2 and 12 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 3, it recites an additional element recitation of “a first feature map included in the first feature map set has a plurality of activations associated with a same layer of the trained neural network” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 4 and 13, they recite an additional limitation of “generating the first plurality of style signals comprises extracting second-order activation information from one or more feature maps included in the first feature map set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create geometric style gradients by mentally gathering or drawing with a pen and paper statistical style information from the calculated second-order activation information from the first object. Para. 0037 teaches, “the style learning engine 160 extracts a different style signal from second-order activation information (statistics) and/or second-order feature information represented in the feature map.” Extracting statistical style information is the second-order activation information, and a person can do this through a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 5 and 14, they recite an additional element recitation of “executing the trained neural network at least once to map the second 3D CAD object to a second feature map set” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 5 and 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 6, it recites an additional limitation of “generating the second plurality of style signals comprises extracting second-order activation information from one or more feature maps included in the second feature map set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create geometric style gradients by mentally gathering or drawing with a pen and paper statistical style information from the calculated second-order activation information for the second object. Para 0037 teaches, “the style learning engine 160 extracts a different style signal from second-order activation information (statistics) and/or second-order feature information represented in the feature map.” Extracting statistical style information is the second-order activation information, and a person can do this through a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 7, it recites an additional limitation of “computing the different partial derivatives of the style comparison metric is further based on the first feature map set and the second feature map set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, after generating the style information, calculating a different partial derivation of the style metrics data with respect its positioning on the 3D CAD object can be conducted using nothing more mathematic calculus formulas. Since the positions in which are utilized in the partial derivative of the style comparison metrics are extracted from the 3D objects which have been mapped to map sets by the neural network (see Para. 38), therefore the derivatives of the style comparison metric are based on the 3D map sets, and derivatives are known mathematical evaluations to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable, (i.e., the feature maps or style signal sets).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 7, it recites an additional limitation of “computing the different partial derivatives of the style comparison metric is further based on the first feature map set and the second feature map set”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally or draw with a pen and paper, after generating the style information on the objects, determine a different partial derivation of the style metrics data with respect its positioning on the 3D CAD object using nothing more mathematic calculus formulas being different partial derivatives to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable (i.e., the positions), since the positions in which are utilized in the partial derivative of the style comparison metrics are extracted from the 3D objects which have been mapped to map sets by the neural network (see Para. 38), therefore the derivatives of the style comparison metric are based on the 3D map sets.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 8, it recites an additional limitation of “wherein each position included in the first plurality of positions comprises an absolute 3D position in a geometric domain and corresponds to a different sample point in a UV domain”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the different partial derivation of the style metrics data on the 3D CAD object for an absolute 3D position with respect its sample UV positioning can be conducted using nothing more mathematic calculus formulas. Since the positions in which are utilized in the partial derivative of the style comparison metrics are extracted from the 3D objects which have been mapped to map sets by the neural network (see Para. 38), therefore the derivatives of the style comparison metric are based on the 3D map sets, and derivatives are known mathematical formulas to correlate the rate of change of an entity with respect to known variable, (i.e., the feature maps or style signal sets).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 8, it recites an additional limitation of “wherein each position included in the first plurality of positions comprises an absolute 3D position in a geometric domain and corresponds to a different sample point in a UV domain”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally or draw with a pen and paper, after generating the style information on the objects, determine the different partial derivation of the style metrics data with respect its positioning on the 3D CAD object for an absolute 3D position with respect its sample UV positioning using nothing more mathematic calculus formulas being different partial derivatives to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable (i.e., the positions), since the positions in which are utilized in the partial derivative of the style comparison metrics are extracted from the 3D objects which have been mapped to map sets by the neural network (see Para. 38), therefore the derivatives of the style comparison metric are based on the 3D map sets.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 9, it recites an additional limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of the style comparison metric for each position included in a second plurality of positions associated with the second 3D CAD object to generate a second geometric style gradient”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the different partial derivation of the style metrics data on the second 3D CAD object be conducted using nothing more mathematic calculus formulas. Since the positions in which are utilized in the partial derivative of the style comparison metrics are extracted from the second 3D object which have been mapped to map sets by the neural network (see Para. 38), therefore the derivatives of the style comparison metric are based on the 3D map sets, and derivatives are known mathematical formulas to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the second 3D CAD object and the first 3D CAD object) with respect to known variable, (i.e., the feature maps or style signal sets).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 9, it recites an additional limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of the style comparison metric for each position included in a second plurality of positions associated with the second 3D CAD object to generate a second geometric style gradient”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, after generating the style information, a person can determine a different partial derivation of the style metrics data with respect its positioning on the 3D CAD object using nothing more mathematic calculus formulas, different partial derivates to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the second 3D CAD object and the first 3D CAD object) with respect to known variable, (i.e., the feature maps or style signal sets).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 9, it recites an additional element recitation of “displaying one or more aspects of the second geometric style gradient via the graphical user interface” which is merely an insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 9 does not recite patent eligible subject matter under 35 U.S.C. §101.
Regarding claim 10, it recites an additional limitation of “first graphical element comprises an arrow or a line that is centered at an absolute 3D position corresponding to the first sample point”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with a pen and paper graphic element that has an arrow centered on the first position used to determine the style gradient from the first 3D object.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 15, it recites an additional element recitation of “computing the different partial derivatives of the style comparison metric comprises performing one or more backpropagation operations via the trained neural network” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 15 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 17, it recites an additional limitation of “first graphical element points in a direction in which to move the first sample point to increase a geometric style similarity between the first 3D CAD object and the second 3D CAD object”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, after generating the style information, a person can create and position or draw with a pen and paper the graphic element on a canvas on the first position of the first 3D object as the gradient with coordinates and magnitude of the gradient to show what direction and how much the first 3D object would need to move at the position to be more similar to the second object. Para. 0044 teaches, “the direction of the vectors included in a gradient between a pair of 3D CAD objects indicate the directions in which corresponding sample points could be moved in the geometry domain to increase the similarity of the geometric styles of the pair of 3D CAD objects. Since the direction of a vector can show an increase in similarity, a person can draw with a pen and paper a vector on one object pointing to a direction where it would need to move in order to become more similar to a same point on a second object.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 18, it recites an additional limitation of “wherein style comparison metric comprises a similarity metric or a distance metric”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating the different partial derivation of a similarity metric with respective to its position on the first 3D object can be conducted using nothing more mathematic calculus formulas. Derivations are known mathematical evaluations to correlate the rate of change of an entity with respect to known variable (i.e., the positions). Para. 0117 teaches, “the partial derivative engine 520 defines the style gradient 540(1) as a set of vectors representing a different partial derivatives of the style comparison metric 170 with respect to each 3D position included in the sample set 328(1),” and Equation 7a shows the style gradient calculation being a function of the partial derivatives of the metric values (Dstyle) and position (sxyz). Therefore, the partial derivative which are mathematical concepts can be used to produce gradients with respect to positions of 3D CAD objects
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 18, it recites an additional limitation of “wherein style comparison metric comprises a similarity metric or a distance metric”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally or draw with a pen and paper, after generating the style information on the objects, calculate a different partial derivation of a similarity metric with respective to its position on the first 3D object using nothing more mathematic calculus formulas being different partial derivatives to correlate the rate of change of an entity (i.e., a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object) with respect to known variable (i.e., the positions).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regard claim 19, it recites an additional limitation of “representation of the first 3D CAD object comprises a boundary representation (B-rep), a 3D point cloud, or a 3D mesh”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with a pen and paper style information of a first 3D object that is represented by a boundary representation to indicate a certain object style such as size and shape. Para. 0058 teaches, “the style signal set provides a representation of stylistic aspects of the 3D CAD object at different scales,” and para. 0038 teaches, “the style comparison metric 170 can be any type of measurement technique that can be used to quantify the similarity or dissimilarity in geometric style between two 3D CAD objects or a ‘pair of 3D CAD objects.’” Therefore, a person can extract stylistic aspects of the 3D object such has its curvature, orientation, or size and use these aspects [style signals] in comparing its similarity to other 3D objects.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, having concluded the analysis within the provided framework, claims 1-15 and 17-20 do not recite patent eligible subject matter and are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, that has not been integrated into a practical application. The claims further do not recite significantly more than the judicial exception. Claims 2-10 and claims 12-15 and 17-19 are also rejected for incorporating the deficiency of their independent claims 1 and 11, respectively.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1, 9-11, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0164055 A1 Ljung Larhed, et al. [herein “Ljung Larhed”] in view of Heimann, et a l. “3D Active Shape Models Using Gradient Descent Optimization of Description Length” Information Processing in Medical Imaging, pp. 566-577 (July 2005) [herein “Heimann”].
As per Claim 1, Ljung Larhed teaches “A computer-implemented method for generating one or more visualizations of at least one geometric style gradient”. (Para. 21, “With the present disclosure, machine learning is used to find an n-dimensional identifier of a 3D object that captures particular features relevant to distinguish that object from other 3D objects. Objects that are similar generate similar feature values” [geometric style]. Para. 44, “The neural network 812 is adjusted so that the 0.43 and 0.48 output values 810 are brought closer together (i.e., a smaller difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.48 value (output) for this particular feature. For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to converge outputs to the same or similar value” [values brought closer to similar value to show similarity, i.e., a gradient]. Para. 28, “The neural network training system 200 in one example uses back propagation or other training techniques. The neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object ( e.g., 3D mesh) that captures particular features relevant to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects (that are not identical)” [using back propagation on the particular features to generate a n-dimensional identifier, e.g., generating a geometric style gradient]. Para. 59, “the method 1000 tunes the neural network to make the outputs similar for the similar training meshes” [e.g., a method for generating]. Para. 0063, “The computing apparatus 1102 may comprise an input/output controller 1118 configured to output information to one or more input devices 1120 and output devices 1122, for example a display” [e.g., a computer-implemented method for generating one or more visualizations of at least one geometric style gradient]. Further see Para 21, 28, 44, 59, and 63. The examiner has interpreted that a method that distinguishes an object from other 3D objects using particular features through the use of back propagation to find an n-dimensional identifier that is adjusted to increase the output value to converge to a similar value for finding similar objects and is outputted through a display device as a computer-implemented method for generating one or more visualizations of at least one geometric style gradient.)
Ljung Larhed also teaches “generating, via execution of a trained neural network, a first plurality of style signals based on a first three dimensional (3D) computer-aided design (CAD) object.” (Para. 21, “the neural network is trained to output relevant distinguishing features by using slightly modified input geometry meshes” [a first plurality of style signals]. Para 21, “machine learning is used to find an n-dimensional identifier of a 3D object that captures particular features relevant to distinguish that object from other 3D objects. Objects that are similar generate similar feature values” [generating, via execution of a trained neural network, a first plurality of style signals based on a first three dimensional (3D) computer-aided design (CAD) object]. Further see Para. 21. The examiner has interpreted the capturing particular relevant features of a 3D object to distinguish that object from other 3D objects using a neural network as generating, via execution of a trained neural network, a first plurality of style signals based on a first three dimensional (3D) computer-aided design (CAD) object.)
Ljung Larhed also teaches “generating, via execution of the trained neural network, a second plurality of style signals based on a second 3D CAD object.” (Para. 28, “the neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object {e.g., 3D mesh} that captures particular features relevant” [a second plurality of style signals style signals] “to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects” [generating, via execution of the trained neural network, a second plurality of style signals based on a second 3D CAD object]. Further see Para. 28. The examiner has interpreted the particular relevant features generating distinguishing feature values for other 3D objects using a neural network as generating, via execution of the trained neural network, a second plurality of style signals based on a second 3D CAD object.)
Ljung Larhed also teaches “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric [for each position included in a first plurality of positions] associated with the first 3D CAD object to generate a first geometric style gradient.” (Para. 21, “With the present disclosure, machine learning is used to find an n-dimensional identifier of a 3D object that captures particular features relevant to distinguish that object from other 3D objects. Objects that are similar generate similar feature values” [geometric style and based on the first plurality of style signals and the second plurality of style signals]. Para. 45, “the search at 906 in one example includes identifying meshes having similar features defined by similar outputs, such as meshes having output values for the relevant properties within a predetermined variance of the values for the relevant features” [style comparison metrics] “of the unknown mesh 902 input into the neural network 900. The trained neural network 900 can be used on the unknown mesh 902 to produce the set of features 904 used to search a database of pre-existing 3D object feature values to identify any similar meshes.” Para. 44, “The neural network 812 is adjusted so that the 0.43 and 0.48 output values 810 are brought closer together (i.e., a smaller difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.48 value (output) for this particular feature. For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to converge outputs to the same or similar value” [values brought closer to similar value to show similarity, i.e., geometric style gradient]. Para. 28, “The neural network training system 200 in one example uses back propagation or other training techniques. The neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object ( e.g., 3D mesh) that captures particular features relevant to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects (that are not identical)” [using back propagation on the particular features to generate a n-dimensional identifier and different partial derivatives are the core of back propagation, e.g., computing a different partial derivative associated with the first 3D object to generate a first geometric style gradient]. On a further note, the examiner has interpreted the relevant properties [i.e., features] as styles and the output values of relevant features as the style comparison metrics. Further see Para 21, 28, 44-45, and 59. The examiner has interpreted that distinguishing an object from other 3D objects using particular features through the use of back propagation to find an n-dimensional identifier that is adjusted to increase the output value for the relevant feature properties to converge to a similar value for finding similar objects as computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric associated with the first 3D CAD object to generate a first geometric style gradient.)
Ljung Larhed also teaches “wherein the first geometric style gradient [includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point] that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object [at the first position]”. (Para. 28, “The neural network training system 200 in one example uses back propagation or other training techniques. The neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object ( e.g., 3D mesh) that captures particular features relevant to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects (that are not identical)” [using back propagation on the particular features to generate a n-dimensional identifier and different partial derivatives are the core of back propagation, e.g., computing the first geometric style gradient].Para. 44, “The neural network 812 is also adjusted so that the 0.43 and 0.12 output values 810 are made farther apart (i.e. a greater difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.12 value (output) for this particular feature” [values diverge further away, e.g., the first geometric style gradient that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object]. “For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to diverge outputs to the dissimilar values. The adjustment is performed for one or all of the output values 810 such that the neural network 812 is trained such that the output values 810 for the first and second meshes 800 and 802 can be identified as being similar and the outputs values 810 for the first and third meshes 800 and 806 can be identified as being dissimilar” [values diverge further away]. Further see Para. 28 and 44. The examiner has interpreted that distinguishing an object from other 3D objects using particular features through the use of back propagation to find an n-dimensional identifier that is adjusted to increase the output value for the relevant feature properties to diverge away from a similar value for finding objects that are dissimilar as wherein the first geometric style gradient that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object.)
Ljung Larhed does not specifically teach a “[computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric] for each position included in a first plurality of positions associated with the first 3D CAD object [to generate a first geometric style gradient] wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object]”, “[wherein the first geometric style gradient] includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point [that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object] at the first position”, “generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient”, and “positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object within a graphical user interface to generate a first visualization of the first geometric style gradient.”
However, in the same field of endeavor namely creating geometric gradients, Heimann teaches “computing, [based on the first plurality of style signals and the second plurality of style signals,] a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object.” (Sect. 3, “Objects of this class are topologically equivalent to a sphere and comprise most shapes encountered in medical imaging ( e.g. liver, kidneys and lungs). The task is to find a one-to-one mapping which assigns every point on the surface of the mesh a unique position on the unit sphere, described by two parameters longitude θ ∈ [0 .. 2π] and latitude φ ∈ [0 .. π]. The mapping of an arbitrary shape to a sphere inevitably introduces some distortion. There are a number of different approaches which attempt to minimize this distortion, typically preserving either local angles or facet areas while trying to minimize distortions in the other…Due to our optimization strategy (Sect. 4), our focus lies on preserving angles: Moving neighboring points on the parameterization sphere in a specific direction, we expect the corresponding landmarks on the training shape to move in a coherent direction as well” [e.g., a geometric style]. Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence on an 3D object, e.g., computing for a geometric style gradient and further generates the gradient graphically to create a visual representation. Section 4.2, “this derivation yields a 3D gradient for every landmark, revealing the influence of its movements on the cost function” [for each position included in a first plurality of positions associated with the first 3D CAD object, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object]. Section 4.2, “the scalars uim and vjm are elements of the matrices U and V from (2). Since our MDL cost function uses λm = d2m, we can derive the MDL gradients as”, see equation 9. The partial derivative of equation 9 computes the gradients for every landmark on the 3D object, e.g., computing a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient. On a further note, partial derivative is used to calculate the gradient, and the examiner has interpreted landmarks as positions on the object. Further see Sect. 3 and 4. The examiner has interpreted that generating a derivation that yields a 3D MDL gradient for every landmark using different partial derivatives to find the point on a mesh which is assigned to a position on a sphere and whose landmarks are moved in a specific direction corresponding to the landmarks on the sphere as computing a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object.)
Heimann also teaches “wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position”. (Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence for 3D objects, e.g., geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object. Figure 2 also shows a graphical element, e.g., a vector, represented by an arrow, and a vector is based on both the direction and magnitude of the geometric gradient. Section 4.2, “this derivation yields a 3D gradient for every landmark, revealing the influence of its movements on the cost function” [wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style and at a first position]. Further see Sect. 4. The examiner has interpreted that generating a 3D MDL gradient for every landmark using different partial derivatives and visualizing the gradient as a vector having both a direction and magnitude as wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position.)
Heimann also teaches “generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient.” (Section 4.2, “the scalars uim and vjm are elements of the matrices U and V from (2). Since our MDL cost function uses λm = d2m, we can derive the MDL gradients as”, see equation 9. Section 4.2, “the direction (Δθ, Δφ) for the movement which minimizes the cost function” [a direction included in the first geometric style gradient]. Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence for the two 3D objects. Figure 2 also shows a graphical element, e.g., a vector, represented by an arrow, and a vector is based on both the direction and magnitude of the geometric gradient. Further see Sect 4. The examiner has interpreted that generating a vector showing the magnitude and direction of the MDL cost function and visualizing that vector on the object as generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient.)
Heimann also teaches “positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object within a graphical user interface to generate a first visualization of the first geometric style gradient.” (Section 4.2, “derivation yields a 3D gradient for every landmark” [positions], “revealing the influence of its movements on the cost function.” The examiner has interpreted Figure 2 as showing graphical element (i.e., a vector, represented by an arrow) at each landmark (i.e., position). Heimann also teaches a vector representing the geometric gradient is generated for a plurality of positions of the 3D objected. Further see Sect. 4. The examiner has interpreted generating an output for the vector is positioned on the object extruding outward on every landmark resembling the 3D gradient as positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object within a graphical user interface to generate a first visualization of the first geometric style gradient.)
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 add the generation of a “[computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric] for each position included in a first plurality of positions [associated with the first 3D CAD object to generate a first geometric style gradient] wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object”, “[wherein the first geometric style gradient] includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point [that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object] at the first position”, “generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient”, and “positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object within a graphical user interface to generate a first visualization of the first geometric style gradient,” as conceptually seen from the teaching of Heimann, into that of Ljung because this modification (1) creates a gradient visualization feature at each position for the advantageous purpose of providing insights into the behavior and appearance of the 3D objects as well as creating more details to more precise style definitions for a more accurate comparison for the entire object, (2) uses partial derivatives for gradient creation for the advantageous purpose of optimizing the 3D model when creating the gradient for each position, (3) creates graphical elements for the advantageous purpose of visually displaying the direction or magnitude of the gradient, and (4) positions the graphical elements for the advantageous purpose of visually displaying where the similar or dissimilar gradients take place on the 3D object surface. Further motivation to combine be that Ljung Larhed and Heimann are analogous art to the current claim and directed to creating geometric gradients.
As per Claim 9, Ljung Larhed teaches “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of the style comparison metric [for each position included in a second plurality of positions] associated with the second 3D CAD object to generate a second geometric style gradient.” (Para. 45, “the search at 906 in one example includes identifying meshes having similar features defined by similar outputs, such as meshes having output values for the relevant properties” [style comparison metrics associated with the second 3D CAD object] “within a predetermined variance of the values for the relevant features of the unknown mesh 902 input into the neural network 900. The trained neural network 900 can be used on the unknown mesh 902 to produce the set of features 904 used to search a database of pre-existing 3D object feature values to identify any similar meshes.” Para. 21, “With the present disclosure, machine learning is used to find an n-dimensional identifier of a 3D object that captures particular features relevant to distinguish that object from other 3D objects. Objects that are similar generate similar feature values” [geometric style and based on the first plurality of style signals and the second plurality of style signals]. Para. 44, “The neural network 812 is adjusted so that the 0.43 and 0.48 output values 810 are brought closer together (i.e., a smaller difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.48 value (output) for this particular feature. For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to converge outputs to the same or similar value” [values brought closer to similar value to show similarity, i.e., geometric style gradient]. Para. 28, “The neural network training system 200 in one example uses back propagation or other training techniques. The neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object ( e.g., 3D mesh) that captures particular features relevant to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects (that are not identical)” [using back propagation on the particular features to generate a n-dimensional identifier and different partial derivatives are the core of back propagation, e.g., computing a different partial derivative associated with the second 3D object to generate a second geometric style gradient]. On a further note, the examiner has interpreted the relevant properties [i.e., features] as styles and the output values of relevant features as the style comparison metrics. Further see Para 21, 28, 44-45, and 59. The examiner has interpreted that distinguishing an object from other 3D objects using particular features through the use of back propagation to find an n-dimensional identifier that is adjusted to increase the output value for the relevant feature properties to converge to a similar value for finding similar objects as computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric associated with the second 3D CAD object to generate a second geometric style gradient.)
Ljung Larhed also teaches “displaying one or more aspects of the second geometric style gradient via the graphical user interface.” (Para. 44, “The neural network 812 is adjusted so that the 0.43 and 0.48 output values 810 are brought closer together (i.e., a smaller difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.48 value (output) for this particular feature. For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to converge outputs to the same or similar value” [one or more aspects of the second geometric style gradient]. Para. 33, “for example, an operator is able to specify the neural network topology using a graphical user interface” [via the graphical user interface]. Para. 63 further teaches “computing apparatus 1102 may comprise an input/output controller” [display] “1118 configured to output information to one or more input devices 1120 and output devices 1122, for example a display or a speaker” [displaying one or more aspects of the second geometric style gradient via the graphical user interface]. Further see Para. 33 and 44. The examiner has interpreted that graphical user interface (GUI) used for displaying outputs of adjusted values through back propagating the neural network to converge outputs to the same or similar value as displaying one or more aspects of the second geometric style gradient via the graphical user interface.)
Ljung Larhed does not specifically teach “[computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of the style comparison metric] for each position included in a second plurality of positions associated with the second 3D CAD object [to generate a second geometric style gradient]”.
However, Heimann teaches a “computing, [based on the first plurality of style signals and the second plurality of style signals,] a different partial derivative of the style comparison metric for each position included in a second plurality of positions associated with the second 3D CAD object to generate a second geometric style gradient.” (Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence on an 3D object, e.g., computing for a geometric style gradient and further generates the gradient graphically to create a visual representation. Section 4.2, “this derivation yields a 3D gradient for every landmark, revealing the influence of its movements on the cost function” [for each position included in a first plurality of positions associated with the second 3D CAD object]. Section 4.2, “the scalars uim and vjm are elements of the matrices U and V from (2). Since our MDL cost function uses λm = d2m, we can derive the MDL gradients as”, see equation 9. The partial derivative of equation 9 computes the gradients for every landmark on the second 3D object, e.g., computing a different partial derivative of a style comparison metric for each position included in a second plurality of positions associated with the second 3D CAD object to generate a second geometric style gradient. On a further note, partial derivative is used to calculate the gradient, and the examiner has interpreted landmarks as positions on the object. Further see Sect. 4. The examiner has interpreted generating a derivation that yields a 3D MDL gradient for every landmark using different partial derivatives as computing a different partial derivative of a style comparison metric for each position included in a second plurality of positions associated with the second 3D CAD object to generate a second geometric style gradient.)
Furthermore, additionally, Heimann also teaches “displaying one or more aspects of the second geometric style gradient via the graphical user interface.” (Fig. 2, the display of gradients that show both magnitude and direction, graphically. Further see Sect 4. The examiner has interpreted the magnitude and the direction are mere aspects of the gradient.)
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 add the “[computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric] for each position included in a second plurality of positions [associated with the second 3D CAD object to generate a second geometric style gradient]”, as conceptually seen from the teaching of Heimann, into that of Ljung because this modification uses partial derivatives for gradient creation for the advantageous purpose of optimizing the 3D model when creating the gradient for each position. Further motivation to combine be that Ljung Larhed and Heimann are analogous art to the current claim and directed to creating geometric gradients.
As per Claim 10, Ljung Larhed teaches “wherein [the first graphical element comprises an arrow or a line that is centered at] an absolute 3D position corresponding to the first sample point.” (Para. 38, “the orientation and size can be normalized to any rotation or size with the object 500 generally positioned in a center of an evaluation area 508, thereby also normalizing position.” Further see Para. 38. The examiner has interpreted the 3D object being positioned in a center of an area as an absolute 3D position corresponding to the first sample point.)
Ljung Larhed does not does not specifically teach that “first graphical element comprises an arrow or a line”.
However, Heimann teaches “first graphical element comprises an arrow or a line” (Section 4.2, “the direction (Δθ, Δφ) for the movement which minimizes the cost function.” Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence on an 3D object. Further see Sect. 4. The examiner has interpreted Figure 2 as showing a graphical element, a vector, represented by an arrow as first graphical element comprises an arrow.)
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 add the generation of a “first graphical element comprises an arrow or a line,” as conceptually seen from the teaching of Heimann, into that of Ljung because this modification creates graphical elements as an arrow for the advantageous purpose of visually displaying the direction and magnitude of the gradient. Further motivation to combine be that Ljung Larhed and Heimann are analogous art to the current claim and directed to creating geometric gradients.
Re Claim 11, it is an articles of manufacture claim, having similar limitations of claim 1. Thus, claim 11 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 11, Ljung Larhed teaches “One or more non-transitory computer readable media including instructions that, when executed by one or more processors” (Para. 62, “computer executable instructions may be provided using any computer-readable media that are accessible by the computing apparatus 1102. Computer-readable media may include, for example, computer storage media such as a memory 1114 and communications media. Computer storage media, such as the memory 1114, include volatile and non-volatile, removable and non-removable media.” Para. 61, “the computing apparatus 1102 comprises one or more processors 1104 which may be microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the electronic device.” Further see Para. 61-62. The examiner has interpreted the processors having computer executable instructions provided by the non-volatile computer-readable media as non-transitory computer readable media including instructions executed by one or more processors.)
As per Claim 15, Ljung Larhed teaches “wherein computing the different partial derivatives of the style comparison metric comprises performing one or more backpropagation operations via the trained neural network.” (Para. 31, “training process including comparing the computed features” [style comparison metrics] “at 216, such as during an iterative back propagation process, wherein the neural network 204 is trained to output similar features for similar meshes” [performing one or more backpropagation operations via the trained neural network]. Further see Para. 31. The examiner has interpreted computing features, the output of the extracted relevant features in iterative back propagation process with a trained neural network as wherein computing the different partial derivatives of the style comparison metric comprises performing one or more backpropagation operations via the trained neural network.)
As per Claim 17, Ljung Larhed teaches “wherein the first [graphical] element points in a direction in which to move the first sample point to increase a geometric style similarity between the first 3D CAD object and the second 3D CAD object.” (Para. 43, “the training process in this example includes adjusting the neural network 812, such as by adjusting the calculating parameters for the neural network 812 so that the corresponding output values 810 for the first and second meshes 800 and 802 are tuned to be close to each other.” Further see Para. 43. The examiner is interpreting the calculated parameters as the data output which is equivalent to the mathematical representation of the graphical element. These parameters are adjusted so the first and second objects are increased in similarity by being tuned close to each other.)
Ljung Larhed does not specifically teach “graphical element”.
However, Heimann teaches “graphical element”. (Section 4.2, “the direction (Δθ, Δφ) for the movement which minimizes the cost function.” Figure 2 displays the gradient descent optimization method to visualize a minimum distance length influence on an 3D object. Further see Sect. 4. The examiner has interpreted Figure 2 as showing a graphical element, a vector, represented by an arrow.)
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 add the generation of a “graphical element,” as conceptually seen from the teaching of Heimann, into that of Ljung because this modification of creating graphical elements for the advantageous purpose of visually displaying the direction or magnitude of the gradient and visually displaying where the similar or dissimilar gradients take place on the 3D object surface. Further motivation to combine be that Ljung Larhed and Heimann are analogous art to the current claim and directed to creating geometric gradients.
As per Claim 19, Ljung Larhed teaches “wherein a representation of the first 3D CAD object comprises a boundary representation (B-rep), a 3D point cloud, or a 3D mesh.” (Para. 28, “machine learning to find an n-dimensional identifier of a 3D object (e.g., 3D mesh).” Further see Para. 28. The examiner has interpreted that 3D mesh as a 3D object.)
Re Claim 20, it is a system claim, having similar limitations of claim 1. Thus claim 20 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 20, Ljung Larhed teaches “A system comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories that, when executing the instructions”. (Para. 23, “The image processing system 100 includes one or more computers 102 and storage 104 to store meshes (e.g., polygon meshes) and images/videos in some examples” [system]. Para. 62, “computer executable instructions may be provided using any computer-readable media that are accessible by the computing apparatus 1102. Computer-readable media may include, for example, computer storage media such as a memory 1114 and communications media. Computer storage media, such as the memory 1114, include volatile and non-volatile, removable and non-removable media." Further details are also shown in Figure 1. Para. 61, "the computing apparatus 1102 comprises one or more processors 1104 which may be microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the electronic device.” Further see Para. 23 and 61-62. The examiner has interpreted that a computer system having computer executable instructions using computer-readable media that are accessible by processors as a system comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories that, when executing the instructions.)
Claims 2-3, 5, 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claims 1 and 11 above, and further in view of Bronstein et al. “Geometric Deep Learning: Going beyond Euclidean data” IEEE Signal Processing Magazine Volume: 34, Issue: 4 (July 2017), [herein “Bronstein”].
As per Claim 2, Ljung Larhed teaches “further comprising executing the trained neural network at least once [to map the first 3D CAD object to a first feature map set].” (Para. 5, “the computerized method further comprises training the neural network using the input mesh and the plurality of training meshes by tuning output of the neural network to identify similar non-identical meshes” [executing the trained neural network at least once]. Further see Para. 5. The examiner has interpreted that identifying similar non-identical meshes by training neural network as executing the trained neural network at least once.)
Ljung Larhed nor Heimann teach “[executing the trained neural network at least once] to map the first 3D CAD object to a first feature map set.”
However, in the same field of endeavor namely using neural networks to find geometric characteristics of 3D objects, Bronstein teaches “executing the trained neural network at least once to map the first 3D CAD object to a first feature map set.” (Sect. 3 Para. 7, “CNN” [neural network] “consists of several convolutional layers of the form g = Cr(f), acting on a p-dimensional input f(x) = (f1 (x), ... , fp(x)) by applying a bank of filters Γ = (ϒl,l'), l = 1, ..., q, l' = 1, ..., p and point-wise non-linearity ξ [equation (6)] producing a q-dimensional output g(x) = (g1(x),..., gq(x)) often referred to as the feature maps” [3D objects are mapped as a feature map using a convolutional neural network, e.g., executing the trained neural network at least once to map the first 3D CAD object to a first feature map set]. Fig. 1 shows that these functions and equations are applied to 3D CAD objects. Further see Sect. 3-4. The examiner is interpreting producing the feature maps for an output for 3D objects through the use of a convolutional neural network as executing the trained neural network at least once to map the first 3D CAD object to a first feature map set.)
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 add “executing the trained neural network at least once to map the first 3D CAD object to a first feature map set” as conceptually seen from the teaching of Bronstein, into that of Ljung Larhed and Heimann combined because this modification maps the 3D object into the map set for the advantageous purpose of transforming a 3D object into a model for the system to mathematically compare to other 3D objects or meshes. Further motivation to combine be that Ljung Larhed, Heimann, and Bronstein are analogous art to the current claim and directed to creating geometric gradients.
As per Claim 3, Ljung Larhed teaches “wherein [a first feature map included in the first feature map set has] a plurality of activations associated with a same layer of the trained neural network.” (Para. 2, “a neural network is a collection of layers of nodes interconnected by edges and where weights which are learned during a training phase are associated with the nodes. Input features are applied to one or more input nodes of the network and propagate through the network in a manner influenced by the weights (the output of a node is related to the weighted sum of the inputs). As a result, activations at one or more output nodes of the network are obtained.” Further see Para. 2. The examiner has interpreted that the layers of the neural network contain nodes where each node contains a plurality of activations as wherein a plurality of activations associated with a same layer of the trained neural network.)
Ljung Larhed nor Heimann specifically teach a “wherein a first feature map included in the first feature map set [has a plurality of activations associated with a same layer of the trained neural network].”
However, Bronstein teaches “wherein a first feature map included in the first feature map set has a first feature map included in the first feature map set”. (Page 4 Para. 7, “CNN” [neural network] “consists of several convolutional layers of the form g = Cr(f), acting on a p-dimensional input f(x) = (f1 (x), ... , fp(x)) by applying a bank of filters Γ = (ϒl,l'), l = 1, ..., q, l' = 1, ..., p and point-wise non-linearity ξ [equation (6)] producing a q-dimensional output g(x) = (g1(x),..., gq(x)) often referred to as the feature maps” [3D objects are mapped as a feature map]. Fig. 1 shows that these functions and equations are applied to 3D CAD objects. Further see Sect. 3-4. The examiner is interpreting producing the feature maps for an output for 3D objects through the use of convolutional layers of a convolutional neural network as wherein a first feature map included in the first feature map set has a plurality of activations associated with a same layer of the trained neural network.)
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 add “wherein a first feature map included in the first feature map set [has a plurality of activations associated with a same layer of the trained neural network]” as conceptually seen from the teaching of Bronstein, into that of Ljung Larhed and Heimann combined because this modification maps the 3D object into the map set for the advantageous purpose of transforming a 3D object into a model for the system to mathematically compare to other 3D models or meshes. Further motivation to combine be that Ljung Larhed, Heimann, and Bronstein are analogous art to the current claim and directed to creating geometric gradients.
Re Claim 5, it is a process claim, having similar limitations of claim 2. Thus claim 5 is also rejected under the similar rationale as cited in the rejection of claim 2.
As per Claim 7, Ljung Larhed teaches “wherein computing the different partial derivatives of the style comparison metric [is further based on the first feature map set and the second feature map set].” (Para. 45, “the search at 906 in one example includes identifying meshes having similar features defined by similar outputs, such as meshes having output values for the relevant properties within a predetermined variance of the values for the relevant features” [style comparison metrics] “of the unknown mesh 902 input into the neural network 900. The trained neural network 900 can be used on the unknown mesh 902 to produce the set of features 904 used to search a database of pre-existing 3D object feature values to identify any similar meshes.” Para. 44, “The neural network 812 is adjusted so that the 0.43 and 0.48 output values 810 are brought closer together (i.e., a smaller difference) by causing an increase in the 0.43 value (output) and a decrease in the 0.48 value (output) for this particular feature. For example, the adjusted values may be passed in the reverse direction (backward propagating) to the neural network 812 to converge outputs to the same or similar value” [values brought closer to similar value to show similarity, i.e., geometric style gradient of the style comparison metric]. Para. 28, “The neural network training system 200 in one example uses back propagation or other training techniques. The neural network training system 200 includes a training processor 202 that uses machine learning to find an n-dimensional identifier of a 3D object ( e.g., 3D mesh) that captures particular features relevant to distinguish the 3D object from other 3D objects, such that a neural network 204 is trained to generate similar feature values for similar objects (that are not identical)” [using back propagation on the particular features to generate a n-dimensional identifier and different partial derivatives are the core of back propagation, e.g., computing a different partial derivatives of the style comparison metric. On a further note, the examiner has interpreted the relevant properties [i.e., features] as styles and the output values of relevant features as the style comparison metrics. Further see Para 21, 28, 44-45, and 59. The examiner has interpreted that using of back propagation to find an n-dimensional identifier that is adjusted to increase the output value for the relevant feature properties to converge to a similar value for finding similar objects as computing the different partial derivatives of the style comparison metric.)
Furthermore, Heimann also teaches “computing the different partial derivatives of the style comparison metric”. (Section 4.2, “the scalars uim and vjm are elements of the matrices U and V from (2). Since our MDL cost function uses λm = d2m, we can derive the MDL gradients as”, see equation 9. The partial derivative of equation 9 computes the gradients for every landmark on the 3D object, e.g., computing a different partial derivative of a style comparison metric. Further see Sect. 4. The examiner has interpreted generating a derivation that yields a 3D MDL gradient for every landmark using different partial derivatives as computing the different partial derivatives of the style comparison metric.)
Neither Ljung Larhed nor Heimann specifically teach “[wherein computing the different partial derivatives of the style comparison metric] further based on the first feature map set and the second feature map set.”
However, Bronstein teaches “[wherein computing the different partial derivatives of the style comparison metric] further based on the first feature map set and the second feature map set.” (Page 4 Para. 7, “CNN” [neural network] “consists of several convolutional layers of the form g = Cr(f), acting on a p-dimensional input f(x) = (f1 (x), ... , fp(x)) by applying a bank of filters Γ = (ϒl,l'), l = 1, ..., q, l' = 1, ..., p and point-wise non-linearity ξ [equation (6)] producing a q-dimensional output g(x) = (g1(x),..., gq(x)) often referred to as the feature maps” [3D objects are mapped as a feature map]. Fig. 1 shows that these functions and equations are applied to 3D CAD objects. Further see Sect. 3-4. The examiner is interpreting producing the feature maps for an output for 3D objects through the use of convolutional layers of a convolutional neural network as further based on the first feature map set and the second feature map set.)
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 add “[computing the different partial derivatives of the style comparison metric] is further based on a first feature map included in the first feature map set” as conceptually seen from the teaching of Bronstein, into that of Ljung Larhed and Heimann combined because this modification maps the 3D object into the map set for the advantageous purpose of transforming a 3D object into a model for the system to mathematically compare to other 3D models or meshes as well as optimizing the performance of the system by not adding a significant computational overload to the system. Further motivation to combine be that Ljung Larhed, Heimann, and Bronstein are analogous art to the current claim and directed to creating geometric gradients.
Re Claim 12, it is an articles of manufacture claim, having similar limitations of claim 2. Thus claim 12 is also rejected under the similar rationale as cited in the rejection of claim 2.
Re Claim 14, it is an articles of manufacture claim, having similar limitations of claim 5. Thus claim 14 is also rejected under the similar rationale as cited in the rejection of claim 5.
Claims 4, 6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed, Heimann, and Bronstein as applied to claims 2 and 12 above, and further in view of US Patent 9,922,432 B1 Risser [herein “Risser”].
Per Claim 4, Ljung Larhed teaches “generating the first plurality of style signals [comprises extracting second-order activation information from one or more feature maps included in the first feature map set].” (Para. 43, “neural network 812 is adjusted so that the output values 810 for the relevant features” [style signals] “converge to the same value or a value within a defined threshold variance that allows for subsequent identification of the first and second meshes 800....neural network 812 is adjusted so that the output values 810 for the relevant features of the first mesh 800 and the third mesh 806 {which are not perceptually similar} diverge to be further apart.” Ljung Larhed teaches a method for creating relevant features values of 3D objects, where similar values to converge and dissimilar values to diverge. The examiner has interpreted style to be any relevant features of the 3D object, such as scale, position and orientation, see Para. 21.)
Ljung Larhed nor Heimann specifically teach “[generating the first plurality of style signals comprises extracting second-order activation information from] one or more feature maps included in the first feature map set.”
However, Bronstein teaches “one or more feature maps included in the first feature map set.” (Page 4 Para. 7, “CNN” [neural network] “consists of several convolutional layers of the form g = Cr(f), acting on a p-dimensional input f(x) = (f1 (x), ... , fp(x)) by applying a bank of filters Γ = (ϒl,l'), l = 1, ..., q, l' = 1, ..., p and point-wise non-linearity ξ [equation (6)] producing a q-dimensional output g(x) = (g1(x),..., gq(x)) often referred to as the feature maps” [3D objects are mapped as a feature map]. Fig. 1 shows that these functions and equations are applied to 3D CAD objects. Further see Sect. 3-4. The examiner is interpreting producing the feature maps for an output for 3D objects through the use of convolutional layers of a convolutional neural network as one or more feature maps included in the first feature map set.)
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 add “[generating the first plurality of style signals comprises extracting second-order activation information from] one or more feature maps included in the first feature map set” as conceptually seen from the teaching of Bronstein, into that of Ljung Larhed and Heimann combined because this modification maps the 3D object into the map set for the advantageous purpose of transforming a 3D object into a model for the system to mathematically compare to other 3D objects or meshes. Further motivation to combine be that Ljung Larhed, Heimann, and Bronstein are analogous art to the current claim and directed to creating geometric gradients.
Neither Ljung Larhed, Heimann nor Bronstein specifically teach “[generating the first plurality of style signals] comprises extracting second-order activation information [from one or more feature maps included in the first feature map set].”
However, in the same field of endeavor namely using neural networks to transfer style, Risser teaches “comprises extracting second-order activation information.” (Col. 12 lines 31-37, “by subtracting off the mean activation before computing inner products, covariance matrices explicitly preserve statistical moments of various orders in the parametric model. By this we explicitly refer to the mean of all feature vectors as the first order moment and to the co-activations of feature vectors centered around their mean as second order moments.” Further see Col. 12. The examiner interprets the first and second order moments of the vector co-activations as the first and second order activation information.)
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 add “comprises extracting second-order activation information” as conceptually seen from the teaching of Risser, into that of Ljung Larhed, Heimann, and Bronstein combined because this modification of using the second order for the advantageous purpose to preserve statistical moments of various orders in the parametric model. Further motivation to combine be that Ljung Larhed, Heimann, Bronstein, and Risser are analogous art to the current claim and directed to using neural networks to transfer style.
Re Claim 6, it is a process claim, having similar limitations of claim 4. Thus claim 6 is also rejected under the similar rationale as cited in the rejection of claim 4.
Re Claim 13, it is an articles of manufacture claim, having similar limitations of claim 4. Thus claim 13 is also rejected under the similar rationale as cited in the rejection of claim 4.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claim 1, and further in view of US 2007/0055401 A1 Van Bael et al. [herein “Van Bael”].
As per Claim 8, Ljung Larhed teaches “wherein each position included in the first plurality of positions comprises an absolute 3D position in a geometric domain [and corresponds to a different sample point in a UV domain].” (Para. 38, “the orientation and size can be normalized to any rotation or size with the object 500 generally positioned in a center of an evaluation area 508, thereby also normalizing position.” Further see Para. 38. The examiner has interpreted the 3D object being position in a center of an area as an absolute 3D position.)
Ljung Larhed nor Heimann teach “corresponds to a different sample point in a UV domain.”
However, in the same field of endeavor namely computing 3D Objects graphically, Van Bael teaches “corresponds to a different sample point in a UV domain.” (Para. 105, “FIG. 22 shows the two points 2103 and 2105 and the line 2107 on the 2D unfolded view of the carton after the (u,v) coordinates have been determined.” Further see Para. 105. The examiner has interpreted that Figure 21 and Figure 22 show that points in the UV domain correspond to the 3D domain.)
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 add that the absolute 3D position “corresponds to a different sample point in a UV domain” as conceptually seen from the teaching of Risser, into that of Ljung Larhed and Heimann combined because this modification of translating the 3D into the 2D for the advantageous purpose to easily visualize aspects and features of 3D objects in the 2D domain to easily compare and visualize the distance variations of the surface of two 3D objects for detecting style changes or similarities. Further motivation to combine be that Ljung Larhed, Heimann, and Van Bael are analogous art to the current claim and directed to computing 3D Objects graphically.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Ljung Larhed and Heimann as applied to claim 11 above, and further in view of US Patent 9,922,432 B1 Risser [herein “Risser”].
As per Claim 18, Ljung Larhed teaches “wherein the style comparison metric [comprises a similarity metric or a distance metric].” (Para. 45, "the search at 906 in one example includes identifying meshes having similar features defined by similar outputs, such as meshes having output values for the relevant properties” [style comparison metrics] “within a predetermined variance of the values for the relevant features of the unknown mesh 902 input into the neural network 900. The trained neural network 900 can be used on the unknown mesh 902 to produce the set of features 904 used to search a database of pre-existing 3D object feature values to identify any similar meshes.” Further see Para. 45. The examiner has interpreted the relevant properties [i.e., features] as styles and the output values of relevant features as the style comparison metrics.)
Ljung Larhed nor Heimann does not does not specifically teach “wherein style comparison metric comprises a similarity metric or a distance metric.”
However, in the same field of endeavor namely using neural networks to transfer style, Risser teaches that the “wherein style comparison metric comprises a similarity metric or a distance metric.” (Col. 10, line 12-15, “content loss can be included as well, where the content loss is some distance metric between raw neural activations calculated for the content image and the image being synthesized.” Further see Col. 10 The examiner has interpreted the content loss is a style comparison metric that is based on the distance.)
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 add “wherein style comparison metric comprises a similarity metric or a distance metric” as conceptually seen from the teaching of Risser, into that of Ljung Larhed and Heimann combined because this modification of using a distance metric for the advantageous purpose to compare the distance variations of the surface of two 3D objects in detecting style changes or similarities. Further motivation to combine be that Ljung Larhed, Heimann, and Risser are analogous art to the current claim and directed to using neural networks to transfer style.
Response to Arguments
Applicant's arguments filed on February 5, 2026 have been fully considered but they are not persuasive.
Applicant argues that the amended claims are patent eligible under 35 U.S.C. § 101 because the claims do not recite mathematical relations, formulas, or calculations (See Applicant’s response, Pg. 9).
MPEP 2106.04(a)(2)(I)(C) recites that a “claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”.
The examiner has provided the rational for the claim limitations that are being directed to a mathematical concept in the rejection above. For example, the claim 1 limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object, wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position” includes calculating a different partial derivative, which is not only a known mathematical calculation, but the formulas have been provided in Equations 7a and 7b of the specification showing a known mathematical operation with the use of a different partial derivative to determine a variable, e.g., the rate of style change between two 3D objects. Additionally, the examiner provided support from the specification (Para. 0043 and 0117) to show that this operation is a mathematical calculation. As such, this limitation would fall under a mathematical concept in step 2A Prong 1 of analysis above.
The examiner has properly identified that the claims recite a mathematical concept as provided in the rejection above under the framework provided in the 2019 Patent Eligibility Guidance and MPEP 2106.04(a)(2)(I)(C). The claims are directed to judicial exception, an abstract idea.
Applicant argues that the amended claims are patent eligible under 35 U.S.C. § 101 because the claims do not recite mental processes (See Applicant’s response, Pg. 9-10).
MPEP 2106.04(a)(2)(III)(C) recites that “claims can recite a mental process even if they are claimed as being performed on a computer” and “in evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”
The examiner has provided the rational for the claim limitations that are being directed to a mental process in the rejection above. For example, the claim 1 limitations of “generating a first plurality of style signals based on a first three dimensional (3D) computer-aided design (CAD) object” and “generating a second plurality of style signals based on a second 3D CAD object”, involves analyzing 3D objects and extracting style information (such as shape, curvature, orientation, size, etc.) from these objects. These limitations can be done by a person merely in the mind by viewing the object and noting the shape and orientation of the object. Additionally, the claim 1 limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object, wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position” includes calculating a different partial derivative, which is not only a known mathematical calculation, but the formulas have been provided in Equations 7a and 7b of the specification showing a known mathematical operation with the use of a different partial derivative to determine a variable, e.g., the rate of style change between two 3D objects. Derivatives have been used in mathematical calculations by people since the 17th century when introduced by Issac Newton. Therefore, the applicant is merely claiming the computer as a tool to perform the abstract idea. Furthermore, the claim 1 limitations of “generating a first graphical element based on at least one of a direction or the magnitude of the first vector included in the first geometric style gradient” and “positioning the first graphical element relative to the first position on a first graphical representation of the first 3D CAD object to generate a first visualization of the first geometric style gradient”, involves drawing a graphical element (such as an arrow, which, as a vector, has both magnitude and direction) that represent the style gradient) and placing the arrow near a drawn object. These limitations can be done by a person in the mind or by drawing with a pen and paper an arrow of the same magnitude and direction as the style gradient and placing the arrow near the drawn object.
Therefore, the examiner has properly identified that the claims recite mental processes as provided in the rejection above under the 2019 Patent Eligibility Guidance and MPEP 2106.04(a)(2) (III)(C). The claims are directed to judicial exception, an abstract idea.
Applicant argues that the amend claim features are patent eligible under 35 U.S.C. § 101 because the claim is integrated into a practical application as claim features recite improvements to another technology or technical field (See Applicant’s response, Pg. 10-13).
MPEP § 2106.04(d)(II) recites “examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
MPEP § 2106.05(a) also recites “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
MPEP 2106.05(a)(II) further recites “ The basis for the McRO court's decision was that the claims were directed to an improvement in computer animation and thus did not recite a concept similar to previously identified abstract ideas. Id. The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. 837 F.3d at 1313, 120 USPQ2d at 1101. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process. 837 F.3d at 1314, 120 USPQ2d at 1102.”
MPEP 2106.05(a) additionally recites “While improvements were evaluated in Alice Corp. as relevant to the search for an inventive concept (Step 2B), several decisions of the Federal Circuit have also evaluated this consideration when determining whether a claim was directed to an abstract idea (Step 2A). See, e.g., Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-16, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016); Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017). Thus, an examiner should evaluate whether a claim contains an improvement to the functioning of a computer or to any other technology or technical field at Step 2A Prong Two and Step 2B”.
Furthermore, MPEP 2106.05(e) recites “The claim should add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter. The phrase "meaningful limitations" has been used by the courts even before Alice and Mayo in various contexts to describe additional elements that provide an inventive concept to the claim as a whole.”
Lastly, MPEP 2106.05(d) recites “The claims in DDR Holdings were directed to systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant. 773 F.3d at 1248, 113 USPQ2d at 1099. The court found that the claim had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the third party’s webpage when the hyperlink was activated. 773 F.3d at 1258-59, 113 USPQ2d at 1106-07.”
The examiner has provided the rational for the independent claim limitations that are being directed to a mental processes and mathematical concepts in the rejection and arguments above. The examiner would like to emphasis the above MPEP citations which show that improvement must come from the additional elements and not the abstract idea itself. The additional elements are “via execution of a trained neural network”, “within a graphical user interface”, “One or more non-transitory computer readable media including instructions that, when executed by one or more processors”, and “A system comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories” which are merely using the generic computer components and functions being used as a tool to perform the abstract idea. Therefore, there are no additional element limitations in the independent claims which can integrate the abstract idea into a practical application by improvements to the technology as listed in MPEP § 2106.04(d)(I). Furthermore, the examiner has also provided the rational for the dependent claim limitations that are being directed to a mental process or a mathematical concept in the rejection above. With the exception of the additional element limitations in the dependent claims which are merely using the generic computer components and functions being used as a tool to perform the abstract idea, there are no additional limitations in the dependent claims which can integrate the abstract idea into a practical application by improvements to the technology or through the use of meaningful limitations.
Therefore, the examiner has properly identified that the claims recite mental processes, mathematical concepts, and limitations that merely use the computer as a tool to perform the abstract idea, insignificant extra-solution activities, or implement the field of use/technological environment
Applicant argues that the combination of references does not teach each and every limitation in the amended claim 1 because cited references fail to teach “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object” and “wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position” (See Applicant’s response, Pg. 14-17).
MPEP 2145(IV) recites “one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references.” Applicant’s reply fails to address the combined teaching of the applied references and instead only argues that each reference individually does not teach all of the claim limitations. One cannot show nonobviousness by attacking reference individually where the rejections are based on combinations of references. MPEP § 2143.03 recites “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
For the limitation of “computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient”, Ljung Larhed discloses computing, based on the first plurality of style signals and the second plurality of style signals, a different partial derivative of a style comparison metric associated with the first 3D CAD object as distinguishing an object from other 3D objects using particular features through the use of back propagation to find an n-dimensional identifier that is adjusted to increase the output value for the relevant feature properties to converge to a similar value for finding similar objects, as provided in the rejection above. It should be noted that adjusting the values using backward propagation so the values make the objects more similar is a different partial derivative of a style comparison metric to generate a first geometric style gradient. Ljung Larhed does not disclose computing these derivatives for each position on the object. Heimann discloses “computing a different partial derivative of a style comparison metric for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, wherein the first plurality of positions corresponds to first plurality of sample points for the first 3D CAD object” as generating a derivation that yields a 3D MDL gradient for every landmark using different partial derivatives to find the point on a mesh which is assigned to a position on a sphere and whose landmarks are moved in a specific direction corresponding to the landmarks on the sphere, as accomplished through the differential partial equations, Equ. 9, and gradients seen in Fig. 2. Points on the sphere are moved in a specific direction to the corresponding landmarks on the training shape through the use of the derivative, e.g., a geometric style comparison metric and this is done for each landmark on the sphere. For the limitation of “wherein the first geometric style gradient includes a first vector associated with a first position included in the first plurality of positions corresponding to a first sample point that indicates a magnitude of a geometric style dissimilarity between the first 3D CAD object and the second 3D CAD object at the first position”, Heimann discloses generating a 3D MDL gradient for every landmark using different partial derivatives and visualizing the gradient as a vector having both a direction and magnitude as accomplished through the differential partial equations, Equ. 9, and gradients seen in Fig. 2 as vectors. Performing the gradient for every landmark includes the first position. By combining the teaching of computing a different partial derivative for each position included in a first plurality of positions associated with the first 3D CAD object to generate a first geometric style gradient, as mapped above, of Heimann into Ljung Larhed the claimed limitation is taught. For example, by inserting the Heimann teaching of computing a different partial derivative for each position of a 3D CAD object to generate a first geometric style gradient which moves the point of the sphere to the specific direction of the training shape as taught by Heimann into the Ljung Larhed teaching of computing the derivative of the style comparison metric to generate the geometric style gradient at each location on the object can be generate the claimed limitations. Additional emphasis and citations have been added to this mapping in the rejection above to the amended limitations.
Therefore, all of the limitations of the amended claim 1 are disclosed in either Ljung Larhed and Heimann, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 as obvious over Ljung Larhed in view of Heimann is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Papaioannidis, Christos, and Ioannis Pitas. "3D object pose estimation using multi-objective quaternion learning." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (2019): 2683-2693.. teaches a method for estimating differences in 3D objects based on the pose of each object. The method using machine learning to determine distance between the objects based on their poses.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Simeon P Drapeau whose telephone number is (571)-272-1173. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET.
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/SIMEON P DRAPEAU/ Examiner, Art Unit 2188
/RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188
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