DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities: possible typos, for example, in paragraph [0027], line 3, states “operation tokes” which should probably be read as “operation tokens” depending on what the Applicant wants to disclose. Examiner recommends checking the rest of the application for similar typos.
Appropriate correction is required.
Response to Amendment
The amendment filed on 08/05/2026 has been entered.
Claims 1, 3, 5-7, 11-13, 17 were amended.
Claims 21-24 was newly added.
Claims 4, 18-20 was/were cancelled.
Claims 1-3, 5-17, 21-24 remain pending in the application.
Response to Arguments
Applicant’s arguments (in remarks filed 08/05/2026) have been considered but are not fully persuasive.
Regarding the Wu Overview on pages 7-8, while not an argument, Applicant makes a few assertions that are contradictions. First, “Wu’s model is therefore confined to sketch-and-extrude paradigm”, this contradicts the other CAD commands in Wu (such as in Table 1), such as the Line or Arc commands. If Wu was confined only to sketch and extrude, no sketching would be possible. Additionally, Applicant states that “Wu contains no command, token, or parameter by which one operation…is defined relative to a geometric entity created by an earlier operation”. Examiner disagrees, looking to Figure 2, Sketch 1, L2 and A3, A3 needs to be relative to the end point of L2 (a geometric entity created by an earlier operation), otherwise, the L2 and A3 geometric entities would not connect at all. Further, L refers to the Line command with parameters shown in Table 1.
Regarding amended independent claim 1, Applicant argues on pg 8-9, reproduced below:
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Upon further review of the reference and in light of applicant's argument, the examiner respectfully disagrees as follows: first of all, the select command has been clarified with the new 103 rejection using the Mr. Blay reference, which shows the select command is essential to the extrude command. For example, Mr. Blay shows a rectangle and circle connected that are both selected to allow for extrusion to occur (Mr. Blay, pg 1, 0:39-0:47, “I typed in extrude and I hit enter. And then I selected the rectangle, and then the circle”). PHOSITA would know, or from limited amount of experimentation, that if only the rectangle was selected, this would result in either an error or an undesired extrusion (or desired depending on PHOSITA’s design goals). Which leads to Wu. While Wu doesn’t explicitly state “select”, Mr. Blay shows that “select” is essential to extrusion. Further, Applicant appears to mention that “Wu consumes the sketch profile”, but there doesn’t appear to any evidence for this “consumption” in the Wu reference. Additionally, as Applicant points out “sketch-plane origin” is part of the parameters of the Extrude command. PHOSITA would know that specific geometric entities would need to be selected, otherwise the undesired extrusion would occur. Sketch plane origin only indicates the origin, not the specific coordinates of the specific sketch for the desired extrusion. Mr. Blay demonstrates how necessary “select” is needed for the extrude command. Further, as Applicant points out at the end about parallel lines, Wu’s Figure 2, Sketch 1, L2 and L4 are parallel lines. If there were no constraints, then the option of non-parallel lines would be in sketch 1.
Regarding amended independent claim 1, Applicant further argues on pages 9-10, reproduced below:
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Upon further review of the reference and in light of applicant's argument, the examiner respectfully disagrees as follows: first of all, Examiner notes that Applicant has ignored the remaining claim mapping, then using unmentioned claim mapping as proof that Wu is silent on extracted practices. Examiner reproduces the image from the claim mapping to provide clarification below:
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. “We use a FeatureScript program to extract the sketch profiles and extrusion”. Which shows extracting practices.
Regarding rejections under 35 USC 103, Applicant has not provided any evidence to their arguments. Therefore, will be ignored.
Accordingly, the claims, as they are currently written, do not place the instant application into an allowable state.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1-3, 5-9, 11-17, 21-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu (“DeepCAD: A Deep Generative Network for Computer-Aided Design Models”, 2021, as cited in IDS filed 02/24/2026), in view of Mr. Blay (“Using Extrude in AutoCAD 2020”, 2020).
Regarding claims 1 and 17, Wu teaches A method for configuring a machine learning model (Wu, see nearest image below, “deep generative network” is being interpreted to involve a machine learning model) to generate geometric models (Wu, pg 1, column 2, ¶1, reproduced below:
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. “construct a 3D shape” is being interpreted as involving a method “to generate geometric models”), the method comprising:
Obtaining (Wu, see nearest image below, “We therefore create a new dataset”, which shows the training obtains data from the dataset) geometric modelling data (Wu, see nearest image below, “We…create a new dataset that…provides CAD command sequences…parse the CAD designs”, which is being interpreted to involve geometric modelling data) comprising sequences of geometric modelling operations (Wu, pg 5, Section 3.3, ¶2-4 and Section 3.4, ¶1, reproduced below:
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. “CAD command sequences” and “commands listed in Table 1” are being interpreted to involve “sequences of geometric modelling operations”); and
training the machine learning model (Wu, see nearest image above, “Leverage the dataset, we train our autoencoder network”) on the geometric modelling data (Wu, see nearest image above, “Leverage the data” is being interpreted to involve “geometric modelling data”) to generate geometric models (Wu, see pg 1, column 2, ¶1 image above, “outputs a sequence of operations used in CAD tools [such as SolidWorks and AutoCAD] to construct a 3D shape”. “Outputs” is being interpreted as involving generating geometric models) encoded as tokenized representations of sequences of geometric modelling operations (Wu, see pg 1, column 2, ¶1 image above, “outputs a sequence of operations used in CAD tools”. Which is being interpreted to involve tokenized representations of sequences of geometric modelling operations) to be performed to build the geometric models (Wu, see pg 1, column 2, ¶1 image above, “outputs a sequence of operations used in CAD tools [such as SolidWorks and AutoCAD] to construct a 3D shape”. “Construct a 3D shape” is being interpreted as involving building the geometric models);
wherein the machine learning model (Wu, pg 5, Section 3.4, ¶1, “Leveraging the dataset, we train our autoencoder”. “Autoencoder” is being interpreted to involve the machine learning model) is trained to generate the geometric models (Wu, see pg 1, column 2, ¶1 image above, “outputs a sequence of operations used in CAD tools [such as SolidWorks and AutoCAD] to construct a 3D shape”. “Outputs” is being interpreted as involving generating geometric models) in accordance with learned geometric modelling practices (Wu, pg 5, Section 3.3 to 3.4 image above, Section 3.4, “Leveraging the dataset, we train our autoencoder”. “Train” is being interpreted to involving “learning”) extracted from the geometric modelling data (Wu, pg 5, Section 3.3 to 3.4 image above, “In the end, we collect a dataset with 178, 238 CAD designs all described as CAD command sequences”).
wherein at least one of the geometric models (Wu, see Section 3.3 image above, “CAD model” is being interpreted as involving geometric models) comprises a plurality of geometric entities (Wu, see Section 3.3 image above, “To create the dataset, we also leverage Onshape’s CAD repository…to parse the CAD designs…For each CAD model”. “CAD designs” and “CAD model” are being interpreted as non-limiting examples of having geometric entities. As stated in paragraph 1 of Section 3.3, “the ABC dataset [23]”, which reference 23 has the title “ABC: A Big CAD Model Dataset For Geometric Deep Learning”. Note “Geometric”), and wherein the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”) for the at least one geometric model (Wu, see Section 3.3 image above, “CAD model”) encodes for a geometric modelling operation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. “Commands” are being interpreted as non-limiting example of “geometric modeling operation”)
…in the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”).
However, Wu does not appear to explicitly teach selecting a geometric entity encoded for earlier. Although, Examiner notes that “Select” command is essential to CAD operations, otherwise, no extrusion may occur (as a non-limiting example), as the Mr. Blay shows.
Pertaining to the same field of endeavor, Mr. Blay teaches
that involves selecting (Mr. Blay, pg 1, 0:39-0:47, “ I typed in extrude and I hit enter. And then I selected”. Which shows “select” command is necessary for extrusion) a geometric entity encoded for earlier (Mr. Blay, pg 1, 0:39-0:47, “And then I selected rectangle”. The “rectangle” is a non-limiting example of geometric entity encoded for earlier).
Wu and Mr. Blay are considered to be analogous art because they are directed to CAD modeling. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of CAD models (as taught by Wu) to include, or try, selecting a geometric entity encoded for earlier (as taught by Mr. Blay) because the combination provides an essential command in order to extrude a sketch (Mr. Blay, pg 1). Further, as Mr. Blay shows on pages 2-5, the rectangle and circle and separately selected. Not only is the “select” command essential to the extrude command, but the desired shapes must be selected. Further, if a user (as the instant specification states in [0027] about “operations employed by the user”), wants to have a hollow or filled in geometry model, other user operations will be required. It would be obvious for PHOSITA to try the predictable paths of hollow or not hollow geometric entity (or CAD model) with the predictable result of it failing or not.
Regarding claim 2, Wu teaches The method of claim 1, wherein the sequences of geometric modelling operations of the geometric modelling data (Wu, pg 1, column 2, ¶1, reproduced below:
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. “sequence of operations used in CAD tools” is being interpreted to involve “sequences of geometric modelling operations of the geometric modelling data”) comprises training data derived from user input into a geometric modelling tool (Wu, supplementary document, Section A, ¶1, reproduced here:
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. “User-created mechanical parts” are being interpreted as involving user input into a geometric modelling tool. “Dataset” is being interpreted as being used for training).
Regarding claim 3, Wu teaches The method of claim 2, wherein a tokenized representation for the at least one geometric model comprises:
a plurality of coordinate tokens representing vertices of the at least one geometric model (Wu, see nearest image below, the Line row has the x, y parameters, which are being interpreted as involving example coordinate tokens that represent vertices of the geometric model); and
one or more operation tokens (Wu, see nearest image below, the CAD commands are being interpreted as involving “one or more operation tokens”) representing one or more geometric modelling operations (Wu, see nearest image below, row 1, the line command is being interpreted as an example of one or more geometric modelling operations) involving one or more of the vertices of the at least one geometric model (Wu, pg 3, Table 1, reproduced below:
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. “Line end-point” from the second row is being interpreted as an example “one or more vertices of the geometric model”), wherein at least one of the one or more geometric modelling operations (Wu, see nearest image above, the CAD commands are being interpreted as involving one or more geometric modelling operations)…
However, Wu does not appear to explicitly teach selecting at least one of the vertices of the at least one geometric model. Although, Examiner notes that “Select” command is essential to CAD operations, otherwise, no extrusion may occur (as a non-limiting example), as the Mr. Blay shows.
Pertaining to the same field of endeavor, Mr. Blay teaches
involves selecting (Mr. Blay, pg 1, 0:39-0:47, “ I typed in extrude and I hit enter. And then I selected”. Which shows “select” command is necessary for extrusion) at least one of the vertices of the at least one geometric model (Mr. Blay, pg 1, 0:39-0:47, “And then I selected rectangle”. The “rectangle” is a non-limiting example of a geometric model. Therefore, the selection tool also selects the vertices of the rectangle).
Wu and Mr. Blay are considered to be analogous art because they are directed to CAD modeling. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of CAD models (as taught by Wu) to include, or try, selecting at least one of the vertices of the at least one geometric model (as taught by Mr. Blay) because the combination provides an essential command in order to extrude a sketch (Mr. Blay, pg 1). Further, as Mr. Blay shows on pages 2-5, the rectangle and circle and separately selected. Not only is the “select” command essential to the extrude command, but the desired shapes must be selected. Further, if a user (as the instant specification states in [0027] about “operations employed by the user”), wants to have a hollow or filled in geometry model, other user operations will be required. It would be obvious for PHOSITA to try the predictable paths of hollow or not hollow geometric entity (or CAD model) with the predictable result of it failing or not.
Regarding claim 5, Wu teaches The method of claim 1, wherein the selection of the geometric entity involves defining one or more coordinates that correspond to the geometric entity (Wu, See Figure 2 image above, Parameterized command sequence, “L_2: (2, 0)” is being interpreted as an example geometric entity that involves defining one or more coordinates that correspond to the geometric entity).
Regarding claim 6, Wu teaches The method of claim 4, wherein the geometric modelling operation…also involves transforming the geometric entity (Wu, see Figure 2 image above, the extrusion from Sketch 1 to Extrude 1 is being interpreted as involving transforming the geometric entity Sketch 1 into Extrude 1).
…in the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”).
However, Wu does not appear to explicitly teach selecting a geometric entity encoded for earlier. Although, Examiner notes that “Select” command is essential to CAD operations, otherwise, no extrusion may occur (as a non-limiting example), as the Mr. Blay shows.
Pertaining to the same field of endeavor, Mr. Blay teaches
…that involves selecting (Mr. Blay, pg 1, 0:39-0:47, “ I typed in extrude and I hit enter. And then I selected”. Which shows “select” command is necessary for extrusion) the geometric entity encoded for earlier … (Mr. Blay, pg 1, 0:39-0:47, “And then I selected rectangle”. The “rectangle” is a non-limiting example of a geometric entity encoded for earlier)…
Wu and Mr. Blay are considered to be analogous art because they are directed to CAD modeling. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of CAD models (as taught by Wu) to include, or try, selecting the geometric entity encoded for earlier (as taught by Mr. Blay) because the combination provides an essential command in order to extrude a sketch (Mr. Blay, pg 1). Further, as Mr. Blay shows on pages 2-5, the rectangle and circle and separately selected. Not only is the “select” command essential to the extrude command, but the desired shapes must be selected. Further, if a user (as the instant specification states in [0027] about “operations employed by the user”), wants to have a hollow or filled in geometry model, other user operations will be required. It would be obvious for PHOSITA to try the predictable paths of hollow or not hollow geometric entity (or CAD model) with the predictable result of it failing or not.
Regarding claim 7, Wu teaches The method of claim 4, wherein the geometric modelling operation involves defining an attribute of a first geometric entity with respect to a second geometric entity (Wu, see Figure 2 image above, “L_4: (0,2)” is being interpreted as an example attribute for a first geometric entity with respect to a second geometric entity, “L_5: (0, 0)”.).
…in the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”).
However, Wu does not appear to explicitly teach selecting a geometric entity encoded for earlier. Although, Examiner notes that “Select” command is essential to CAD operations, otherwise, no extrusion may occur (as a non-limiting example), as the Mr. Blay shows.
Pertaining to the same field of endeavor, Mr. Blay teaches
…that involves selecting (Mr. Blay, pg 1, 0:39-0:47, “ I typed in extrude and I hit enter. And then I selected”. Which shows “select” command is necessary for extrusion) the geometric entity encoded for earlier … (Mr. Blay, pg 1, 0:39-0:47, “And then I selected rectangle”. The “rectangle” is a non-limiting example of a geometric entity encoded for earlier)…
Wu and Mr. Blay are considered to be analogous art because they are directed to CAD modeling. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of CAD models (as taught by Wu) to include, or try, selecting the geometric entity encoded for earlier (as taught by Mr. Blay) because the combination provides an essential command in order to extrude a sketch (Mr. Blay, pg 1). Further, as Mr. Blay shows on pages 2-5, the rectangle and circle and separately selected. Not only is the “select” command essential to the extrude command, but the desired shapes must be selected. Further, if a user (as the instant specification states in [0027] about “operations employed by the user”), wants to have a hollow or filled in geometry model, other user operations will be required. It would be obvious for PHOSITA to try the predictable paths of hollow or not hollow geometric entity (or CAD model) with the predictable result of it failing or not.
Regarding claim 8, Wu teaches The method of claim 7, wherein the attribute is a geometric constraint (Wu, see Figure 2 image above, the Sketch 1 to Extrude 1 extrusion height is being interpreted as an attribute for a geometric constraint. Otherwise, the extrusion would go into infinity or not work at all).
Regarding claim 9, Wu teaches The method of claim 1, wherein the learned geometric modelling practices extracted from the geometric modelling data comprise tendencies to apply different geometric modelling techniques (Wu, see Figure 2 image above, line creation and circle creation are being interpreted as example geometric modelling techniques) in different geometric modelling scenarios (Wu, see Figure 2, Sketch 1 and Sketch 2 are being interpreted as involving different scenarios).
Regarding claim 11, Wu teaches The method of claim 1, wherein the machine learning model is configured to apply self-attention (Wu, Abstract: “We propose a CAD generative network based on the Transformer”; Wu, pg 2, column 2, Subsection “Transformer-based models”, ¶1: “what was introduced as an attention-based building block for many natural language processing tasks [13]”. Reference 13, as one with ordinary skill would know, has self-attention) among the elements of the tokenized representation (Wu, Section 3.2. Autoencoder for CAD models, ¶1: “We now introduce an autoencoder network that leverages our representation of CAD commands.” Which is being interpreted to involve the elements of the tokenized representation) of the at least one geometric model (Wu, Section 3.2 title: “Autoencoder for CAD models”. “CAD models” are being interpreted as a non-limiting example of having at least one geometric model).
Regarding claim 12, Wu teaches The method of claim 11, wherein the machine learning model is further configured to apply cross-attention (Wu, Abstract: “We propose a CAD generative network based on the Transformer”; Wu, pg 2, column 2, Subsection “Transformer-based models”, ¶1: “what was introduced as an attention-based building block for many natural language processing tasks [13]”. Reference 13, as one with ordinary skill would know, has cross-attention. As seen in Section 3.2, ¶1 from reference 13) between the elements of the tokenized representation of the at least one geometric model and a context token (Wu, Section 3.2, ¶1-2, reproduced below:
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. “Embedding” is being interpreted to involve tokenized representation. CAD example command from Table 1, Circle, is being interpreted as involving “context token”. The section title that has “CAD models” is being interpreted as having “at least one geometric model”).
Regarding claim 13, Wu teaches A method for generating geometric models (Wu, see nearest image below, “deep generative network that outputs a sequence of operations used in CAD tools”), the method comprising:
applying a machine learning model (Wu, see nearest image below, “Output” from the “deep generative network” is being interpreted as involving applying a machine learning model) to generate a tokenized representation of a geometric model (Wu, Section 3.2, ¶2, “each command…is projected onto a continuous embedding space” is being interpreted to involve “tokenized representation”. The command is involved with CAD command, which involves “a geometric model”), wherein the tokenized representation of the geometric model (Wu, see nearest image below, “deep generative network” is being interpreted to involve “tokenized representation of the geometric model”) defines a sequence of geometric modelling operations (Wu, see nearest image below, “operational sequence” is being interpreted as involving “a sequence of geometric modelling operations”) that is to be performed to build the geometric model (Wu, pg 1, column 2, ¶1, reproduced below:
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. “construct a 3D shape” is being interpreted as involving a method “to build the geometric model”. “Output” from the “deep generative network” is being interpreted as involving applying a machine learning model.);
wherein the geometric model (Wu, see Section 3.3 image above, “CAD model” is being interpreted as involving geometric models) comprises a plurality of geometric entities (Wu, see Section 3.3 image above, “To create the dataset, we also leverage Onshape’s CAD repository…to parse the CAD designs…For each CAD model”. “CAD designs” and “CAD model” are being interpreted as non-limiting examples of having geometric entities. As stated in paragraph 1 of Section 3.3, “the ABC dataset [23]”, which reference 23 has the title “ABC: A Big CAD Model Dataset For Geometric Deep Learning”. Note “Geometric”), and the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”) encodes for a geometric modelling operation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. “Commands” are being interpreted as non-limiting example of “geometric modeling operation”) … in the tokenized representation (Wu, see Section 3.3 image above, “For the rest of the models, we use a FeatureScript program to extract the sketch profiles and extrusions, and express them using the commands listed in Table 1”. The commands are being interpreted as involving “tokenized representation”, as the instant specifications state on [0027]: “The tokenized representation 220 includes a series of operation toke[n]s and coordinate tokens that encode for the sequence of geometric modelling operations employed by the user”. The non-limiting example of CAD commands are being interpreted as involving “operation tokens”).
However, Wu does not appear to explicitly teach selecting a geometric entity encoded for earlier. Although, Examiner notes that “Select” command is essential to CAD operations, otherwise, no extrusion may occur (as a non-limiting example), as the Mr. Blay shows.
Pertaining to the same field of endeavor, Mr. Blay teaches
…that involves selecting (Mr. Blay, pg 1, 0:39-0:47, “ I typed in extrude and I hit enter. And then I selected”. Which shows “select” command is necessary for extrusion) a geometric entity encoded for earlier (Mr. Blay, pg 1, 0:39-0:47, “And then I selected rectangle”. The “rectangle” is a non-limiting example of geometric entity encoded for earlier).
Wu and Mr. Blay are considered to be analogous art because they are directed to CAD modeling. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of CAD models (as taught by Wu) to include, or try, selecting a geometric entity encoded for earlier (as taught by Mr. Blay) because the combination provides an essential command in order to extrude a sketch (Mr. Blay, pg 1). Further, as Mr. Blay shows on pages 2-5, the rectangle and circle and separately selected. Not only is the “select” command essential to the extrude command, but the desired shapes must be selected. Further, if a user (as the instant specification states in [0027] about “operations employed by the user”), wants to have a hollow or filled in geometry model, other user operations will be required. It would be obvious for PHOSITA to try the predictable paths of hollow or not hollow geometric entity (or CAD model) with the predictable result of it failing or not.
Regarding claim 14, Wu teaches The method of claim 13, further comprising:
converting the tokenized representation (Wu, see nearest image below, “embedding” which is related to latent vector z, is being interpreted to involve “tokenized representation”. The decoder that takes latent vector z and outputs a generated CAD command sequence is being interpreted as involving “converting”) of the geometric model into a format suitable for use in a geometric modelling environment (Wu, Section 3.2, ¶1-2, reproduced below:
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. “CAD command sequences” is being interpreted as involving “format suitable for use in a geometric modelling environment”, as CAD is being interpreted to involve “geometric modelling environment”); and
instantiating the geometric model in a geometric modelling environment (Wu, pg 1, column 2, ¶1, “We propose a deep generative network that outputs a sequence of operations used in CAD tools (such as SolidWorks and AutoCAD) to construct a 3D shape”. Which shows it would be obvious to instantiate the geometric model in a geometric modelling environment).
Regarding claim 15, Wu teaches The method of claim 13, wherein the machine learning model is trained (Wu, Section 3.2, ¶1, the autoencoder network is being interpreted as “the machine learning model is trained”) to generate tokenized representations (Wu, Section 3.2, ¶2, “embedding” is being interpreted as involving “tokenized representations”, the encoding processing is being interpreted as involving generating) of geometric models of particular object classes (Wu, Table 1, line, arc, circle, commands are being interpreted as examples of “geometric models of particular object classes”).
Regarding claim 16, Wu teaches The method of claim 13, wherein the machine learning model is trained to generate tokenized representations of building structures (Wu, Table 1, Line, Arc, and Circle commands are being interpreted as involving “generate tokenized representations of building structures”).
Claim 21 is rejected using the same rationale and motivation as applied to claim 3 discussed above.
Claim 22 is rejected using the same rationale and motivation as applied to claim 9 discussed above.
Claim 23 is rejected using the same rationale and motivation as applied to claim 14 discussed above.
Claim 24 is rejected using the same rationale and motivation as applied to claim 16 discussed above.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu, in view of Nash (“PolyGen: An Autoregressive Generative Model of 3D Meshes”, 2020, as cited in IDS filed 02/24/2026).
Regarding claim 10, Wu teaches The method of claim 1, wherein the machine learning model
However, Wu does not appear to explicitly teach an autoregressive generative model.
Pertaining to the same field of endeavor, Nash teaches
comprises an autoregressive generative model (Nash, pg 1, column 2, first partial paragraph: “….we present PolyGen, a neural generative model of meshes that autoregressively estimates a joint distribution over mesh vertices and faces”).
Wu and Nash are considered to be analogous art because they are directed to machine learning model generation of geometry. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for machine learning model generation of geometry (as taught by Wu) to include, or try, an autoregressive generative model (as taught by Nash) because the combination provides an improvement to the quality of models (Nash, Abstract).
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al (US 10769848 B1) discloses machine learning model to generate geometric models (interpreted from mesh representation).
Zhang et al (“Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training”, May 2022) discloses machine learning model that generates tokenized representation of geometry (see Figure 1).
Fan et al (“CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings”, June 2022) discloses machine learning model that generates tokenized representation of CAD geometry.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY B DUONG whose telephone number is (571)272-1358. The examiner can normally be reached Monday - Thursday 10a-9p (ET).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.B.D./Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
