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 .
Status of Claims
This action is in response to the amendments filed 25 May 2026.
Claims 1-20 are pending.
Of the pending claims, 1, 8, and 15 are amended.
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.
Claims 1-20 are rejected under 35 U.S.C. 103.
Claims 1, 2, 8, 14, 15, and 20
Claims 1-2, 8, 14-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao et al. ("Applying a Hybrid Approach Based On Fuzzy Neural Network and Genetic Algorithm to Product Form Design", hereinafter "Hsiao") in view of Aggarwal et al. (U.S. 20230012650 hereinafter Aggarwal).
Claim 1
Regarding claim 1, Hsiao teaches a computer-implemented method of creating a prototype, the method comprising:
receiving, by a first neural network (Hsiao, pg. 415, section 2.2, third paragraph, “Prior to using the BPN [backpropagating neural network] in practice, it must first undergo a training process, in which it is assumed that the product form is generated by an input parameter set: v1, v2, …, vn (i.e. the input-node code) and a corresponding fuzzy number, ~tout, for the related image sensation”), one or more input design parameters (Hsiao (paragraph above figure 9), “the customized interface used for constructing a 3-D model of an electronic door lock is shown in Fig. 9. In the parameter-setting window, the designer is invited to specify the dimensions of the required form by regulating the scroll bar or by selecting the appropriate options for each form parameter. When all of the parameters have been set, the designer presses the ‘‘Show 3-D models’’ button to dynamically browse the exported VRMLfile”);
retrieving, product image data (Hsiao (pg. 420, section 3.5, first paragraph) “The 10 models presented in Fig. 6 and the four selected image word-pairs from Table 3 (indicated by the ‘a’ notation) were used as initial samples to establish the relationships between the product forms and their images via a series of e-questionnaires.”) of currently existing products corresponding based on the one or more input design parameters (Hsiao (paragraph above figure 9), “the customized interface used for constructing a 3-D model of an electronic door lock is shown in Fig. 9. In the parameter-setting window, the designer is invited to specify the dimensions of the required form by regulating the scroll bar or by selecting the appropriate options for each form parameter. When all of the parameters have been set, the designer presses the ‘‘Show 3-D models’’ button to dynamically browse the exported VRMLfile”. Hsiao (pg. 419, left column, first paragraph lines 10-16), “Using this function, an interface was developed which enabled the designer to modify the design parameters and to browse the constructed 3-D models from a remote PC. In the current study, the interface was used to construct 10 new product forms (basic test samples) exhibiting obvious shape differences.”), from a data source, wherein the data source comprises at least one of an internet source or a user provided source (Hsiao (pg. 420, section 3.5, first paragraph) “The 10 models presented in Fig. 6 and the four selected image word-pairs from Table 3 (indicated by the ‘a’ notation) were used as initial samples to establish the relationships between the product forms and their images via a series of e-questionnaires.”), and wherein the retrieving is based on a result of a data search (Hsiao (pg. 423, right column, line 1-4), “The designer can then select the acceptable model as a test sample and then create blended models for subsequent image evaluation testing”. User searches for an acceptable model for image evaluation testing) wherein the first neural network comprises a discriminator trained on one or more of the product image data or historical user preference data (Hsiao (pg. 415, second paragraph, right column), “in which it is assumed that the product form is generated by an input parameter set: v1, v2, y, vn (i.e. the input node-code) and a corresponding fuzzy number,~tout, for the related image sensation”; discriminator is used to generate image sensation while predict model generates the product form. Hsiao (pg. 421, section 3.6.1, line 1-9), “training of the network involved 37 test samples for eachword- pair. Note that since four image word-pairs were selected, it was necessary to train a network for each word pair. Under the condition of an root mean square error (RMSE ) value lower than 0.1, the number of learning iterations was specified as 100,000 epochs in order to obtain the best possible values of the network’s weights and biases”);
generating, using the first neural network (Hsiao (pg. 415, second paragraph, right column), “in which it is assumed that the product form is generated by an input parameter set: v1, v2, y, vn (i.e. the input node-code) and a corresponding fuzzy number, ~tout, for the related image sensation”), a plurality of prototypes based on the one or more input design parameters (Hsiao (pg. 419, first paragraph, left column), “In the current study, the interface was used to construct 10 new product forms (basic test samples) exhibiting obvious shape differences. The corresponding rendered models are shown in Fig. 6, together with their perspective views. The detailed dimensions of these models are provided in Table 2.” Fig. 2 shows different designs are generated from the same parameters);
While Hsiao teaches generating prototypes with neural networks, Hsiao does not explicitly teach generating decoy prototypes. However, Aggarwal teaches:
generating a decoy prototype by a second neural network based on at least one prototype of the plurality of prototypes (Aggarwal (¶0054, fig. 6 item 606), “includes creating, using the machine learning framework, one or more refurbished designs of each given one of the plurality of products based on the initial image of the given product and one or more design constraints”), wherein the second neural network is trained on marketplace data (Aggarwal (¶0054, fig. 6 item 606), “calculating, by the machine learning framework, an environmental impact score and a demand impact score associated with each of the created refurbished designs (decoy prototype), wherein the demand impact score is based at least in part on the location-specific demand data”);
generating one or more prototypes by modifying the decoy prototype by the second neural network , wherein the modifying comprises running the second neural network based on a target effect on at least one attribute of the decoy prototype (Aggarwal (¶0054, fig. 6 item 606), “includes creating, using the machine learning framework, one or more refurbished designs of each given one of the plurality of products based on the initial image of the given product and one or more design constraints”); and
presenting, by an electronic display, a report comprising at least a portion of the plurality of prototypes and at least one of the one or more decoy prototypes (Aggarwal (¶0030, fig. 3), “this figure shows a counterfactual formulation process in accordance with exemplary embodiments. In the FIG. 3 example, the counterfactual formulation process 302 is performed with respect to an image of a first product 300 and an image of a refurbished product 304. As can be seen from FIG. 3, the refurbished product 304 includes some modifications 306 relative to product 300”).
Hsiao and Aggarwal are considered analogous to the claimed invention as they are in the field of prototype design and development. Aggarwal teaches the development of decoy prototypes with design parameters, while Hsiao teaches the development of prototypes with design parameters through use of a neural network. It would have been obvious to a person having ordinary skill in the art (hereinafter “PHOSITA”), before the effective filing date of the invention, to incorporate the teachings of Aggarwal into the design of Hsiao. The benefit of making this change is to “an exemplary embodiment includes an artificial intelligence-based system that is configured to discover products and refurbishing schemes for products in unsold inventory” (Aggarwal (¶0016 line 1-4)).
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1, further comprising fabricating at least one select prototype of the plurality of prototypes and the one or more decoy prototypes (Hsiao, pg. 426, left col., first paragraph, “The CAD models… can then be input to the RP (Rapid Prototyping) machine to produce physical prototypes”; Hsiao explains, in the Abstract, “a feature-based hierarchical computer-aided design (CAD) model is constructed, in which the related form parameters are thoroughly defined in applicable domains to facilitate the automatic generation of new product forms”).
Claim 5
Regarding claim 5, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1 further comprising receiving, by the second neural network, competitive data relating to a product corresponding to the one or more input design parameters (Aggarwal (¶0054, fig. 6 item 606), “includes creating, using the machine learning framework, one or more refurbished designs of each given one of the plurality of products based on the initial image of the given product and one or more design constraints”).
Claim 8
Regarding claim 8, Hsiao teaches:
a system for creating a prototype (Hsiao, Abstract, “The proposed method provides an automatic design system, which gives designers the ability to rapidly obtain a product form and its corresponding image”), the system comprising:
one or more processors (Hsiao, pgs. 423-4, figs. 9-11 show screenshots from a computer display, indicating the display is part of a system containing a processor);
an electronic display (Hsiao, pgs. 423-4, figs. 9-11 show screenshots from a computer display, indicating the presence of an electronic display);
one or more non-transitory memory modules storing computer-readable instructions (Hsiao, pgs. 423-4, figs. 9-11 show screenshots from a computer display, indicating the display is part of a system containing a processor and inherent non-transitory memory units containing computer instructions for running the process shown in the screenshots) that, when executed, cause the one or more processors to
The rest of the limitation(s) are rejected for the same reasons as Claim 1.
Claim 11
Regarding claim 11, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, wherein the computer-readable instructions further cause the one or more processors to receive, by the second neural network, competitive data relating to a product corresponding to the one or more input design parameters (Aggarwal (¶0054, fig. 6 item 606), “includes creating, using the machine learning framework, one or more refurbished designs of each given one of the plurality of products based on the initial image of the given product and one or more design constraints”).
Claim 14
Regarding claim 14, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, further comprising a three-dimensional printer, wherein the three-dimensional printer is configured to fabricate at least a portion of at least one prototype of the plurality of prototypes (Hsiao, pg. 426, left col., first paragraph, “The CAD models… can then be input to the RP (Rapid Prototyping) machine to produce physical prototypes”; Hsiao explains, in the Abstract, “a feature-based hierarchical computer-aided design (CAD) model is constructed, in which the related form parameters are thoroughly defined in applicable domains to facilitate the automatic generation of new product forms”).
Claim 15
Regarding claim 15, Hsiao teaches:
a computer-implemented method of fabricating a prototype (Abstract, “The proposed method provides an automatic design system, which gives designers the ability to rapidly obtain a product form and its corresponding image”), the method comprising:
fabricate at least one select prototype of one or more of the plurality of prototypes or the one or more decoy prototypes (Hsiao (pg. 426 left column, second paragraph), “two examples shown in Figs. 11 and 15(f) can then be input to the RP (Rapid Prototyping) machine to produce physical prototypes.”).
The rest of the limitation(s) are rejected for the same reasons as Claim 1.
Claim 18
Regarding claim 18, the rejection of claim 15 is incorporated. Further, the combination of Hsiao in view of Aggarwal in further view of Gandhi teaches the computer-implemented method of claim 15, but does not teach the limitation further comprising receiving, by the second neural network, competitive data relating to a product corresponding to the one or more input design parameters (Aggarwal (¶0054, fig. 6 item 606), “includes creating, using the machine learning framework, one or more refurbished designs of each given one of the plurality of products based on the initial image of the given product and one or more design constraints”).
Claim 20
Regarding claim 20, the rejection of claim 15 is incorporated. Further, the combination of Hsiao in view of Aggarwal in further view of Gandhi teaches wherein receiving the plurality of prototypes comprises receiving a report comprising ranked prototypes (Aggarwal (¶0029 last 4 lines), “The counterfactual optimization module 128 may generate a ranked list of refurbished designs using population-based search algorithms (such as NSGA-II, for example).”).
Claims 3, 9, and 16
Claims 3, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Aggarwal as applied to claims 1, 8, and 15 above, and further in view of Krahe et al. ("Deep Learning for Automated Product Design", hereinafter "Krahe").
Claim 3
Regarding claim 3, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1, but does not teach the limitation wherein the first neural network comprises a generative adversarial network (GAN). However, Krahe teaches this limitation (Krahe, pg. 4, right col., last paragraph, “The approach developed is based on the generative model presented in [4] consisting of an AE [autoencoder] and a GAN. The AE converts the 3D objects into a latent vector representation. Since training and use of the GAN is fully based on these latent vectors, the GAN is called l-GAN (latent GAN) and is independent of the input data format”; the approach is specifically in relation to product design).
Krahe is considered analogous to the claimed invention since they are in the field of product design via machine learning. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Krahe into the teachings of Hsiao in view of Aggarwal. The benefit of making this change is that “more recent approaches, as presented in [4,20–26], focus on generative and discriminative representations for geometry… They make use of a combination of AEs and generative models, such as GANs [3], to create new 3D objects” (Krahe, pg. 4, right col., second paragraph).
Claim 9
Regarding claim 9, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, but does not teach the limitation wherein the first neural network comprises a generative adversarial network (GAN). However, Krahe teaches this limitation (Krahe, pg. 4, right col., last paragraph, “The approach developed is based on the generative model presented in [4] consisting of an AE [autoencoder] and a GAN. The AE converts the 3D objects into a latent vector representation. Since training and use of the GAN is fully based on these latent vectors, the GAN is called l-GAN (latent GAN) and is independent of the input data format”; the approach is specifically in relation to product design, and use of the neural network is done via computer instructions).
It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Krahe into the teachings of Hsiao in view of Aggarwal. The benefit of making this change is that “more recent approaches, as presented in [4,20–26], focus on generative and discriminative representations for geometry… They make use of a combination of AEs and generative models, such as GANs [3], to create new 3D objects” (Krahe, pg. 4, right col., second paragraph).
Claim 16
Regarding claim 16, the rejection of claim 15 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 15, but does not teach the limitation wherein the first neural network comprises a generative adversarial network (GAN). However, Krahe teaches this limitation (Krahe, pg. 4, right col., last paragraph, “The approach developed is based on the generative model presented in [4] consisting of an AE [autoencoder] and a GAN. The AE converts the 3D objects into a latent vector representation. Since training and use of the GAN is fully based on these latent vectors, the GAN is called l-GAN (latent GAN) and is independent of the input data format”; the approach is specifically in relation to product design).
Krahe is considered analogous to the claimed invention since they are in the field of product design via machine learning. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Krahe into the teachings of Hsiao in view of Aggarwal. The benefit of making this change is that “more recent approaches, as presented in [4,20–26], focus on generative and discriminative representations for geometry… They make use of a combination of AEs and generative models, such as GANs [3], to create new 3D objects” (Krahe, pg. 4, right col., second paragraph).
Claims 4, 10, and 17
Claims 4, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Aggarwal as applied to claims 1, 8, and 15 above, and further in view of Edwards et al. (U.S. Patent No. 10,650,358; hereinafter "Edwards").
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1, but does not teach the limitation further comprising receiving, by the first neural network, historical product data for training the first neural network. However, Edwards teaches this limitation (Edwards, paragraph 36, “assume the document management platform trained or received a data model that had been trained on historical warranty data. The historical warranty data may include… historical product data for the product to which the warranty applies, and/or the like. The data model may use one or more machine learning techniques… The one or more machine learning techniques may include… a technique using a neural network, and/or the like”; paragraph 37, “the document management platform may provide the transaction information… as input to the data model”; Edwards discloses a neural network model trained on historical data such as warranty data or transaction data, which can be considered product data).
Edwards is considered analogous to the claimed invention since they are in the same field of endeavor of utilizing machine learning techniques to achieve a stated goal. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Edwards into that of Hsiao in view of Aggarwal; it is inherent that a neural network will output data related to that which it is trained on, so using historical product data as training data is considered a simple substitution leading to a predictable result.
Claim 10
Regarding claim 10, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, but does not teach the limitation wherein the computer-readable instructions further cause the one or more processors to receive, by the first neural network, historical product data for training the first neural network. However, Edwards teaches this limitation (Edwards, paragraph 36, “assume the document management platform trained or received a data model that had been trained on historical warranty data. The historical warranty data may include… historical product data for the product to which the warranty applies, and/or the like. The data model may use one or more machine learning techniques… The one or more machine learning techniques may include… a technique using a neural network, and/or the like”; paragraph 37, “the document management platform may provide the transaction information… as input to the data model”; Edwards discloses a neural network model trained on historical data such as warranty data or transaction data, which can be considered product data, and use of the neural network is done via computer instructions).
Edwards is considered analogous to the claimed invention since they are in the same field of endeavor of utilizing machine learning techniques to achieve a stated goal. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Edwards into that of Hsiao in view of Aggarwal; it is inherent that a neural network will output data related to that which it is trained on, so using historical product data as training data is considered a simple substitution leading to a predictable result.
Claim 17
Regarding claim 17, the rejection of claim 15 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 15, but does not teach the limitation further comprising receiving, by the first neural network, historical product data for training the first neural network. However, Edwards teaches this limitation (Edwards, paragraph 36, “assume the document management platform trained or received a data model that had been trained on historical warranty data. The historical warranty data may include… historical product data for the product to which the warranty applies, and/or the like. The data model may use one or more machine learning techniques… The one or more machine learning techniques may include… a technique using a neural network, and/or the like”; paragraph 37, “the document management platform may provide the transaction information… as input to the data model”; Edwards discloses a neural network model trained on historical data such as warranty data or transaction data, which can be considered product data).
Edwards is considered analogous to the claimed invention since they are in the same field of endeavor of utilizing machine learning techniques to achieve a stated goal. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Edwards into that of Hsiao in view of Aggarwal; it is inherent that a neural network will output data related to that which it is trained on, so using historical product data as training data is considered a simple substitution leading to a predictable result.
Claims 6, 7, 12, 13, and 19
Claims 6-7, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Aggarwal as applied to claims 1, 8, and 15 above, and further in view of Felfernig et al. ("A Dominance Model for the Calculation of Decoy Products in Recommendation Environments", hereinafter "Felfernig").
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1, but does not teach the limitation further comprising filtering the plurality of prototypes to eliminate one or more prototypes of the plurality of prototypes. However, Felfernig teaches this limitation (Felfernig, pg. 46, right col., first paragraph, “It is important to note that the application of the SDM [simple dominance model] happens after the recommender has filtered out completely unsuitable items (e.g.: items exceeding a minimum/maximum threshold on a certain attribute)”; Felfernig explains in the previous paragraph that these items are grouped with decoy items that increase their dominance value, and items not within a certain attribute or quality range are discarded).
Felfernig is considered analogous to the claimed invention since they are in the same field of product and decoy design. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Felfernig into the teachings of Hsiao in view of Aggarwal. As the Hsiao combination does not include any form of filtering prototypes, Felfernig’s teachings can be added as an improvement. This would be applying a known technique to a known device (method or product) ready for improvement to yield predictable results.
Claim 7
Regarding claim 7, the rejection of claim 1 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 1, but does not teach the limitation further comprising ranking the plurality of prototypes. However, Felfernig teaches this limitation (Felfernig, pg. 43, right col., list item 3, “After the utility calculation of the products the system typically presents the top ranked products to the user”; the products are a combination of items and decoy items as explained on page 46).
It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Felfernig into the teachings of Hsiao in view of Aggarwal. As the Hsiao combination does not include any form of ranking prototypes, Felfernig’s teachings can be added as an improvement. This would be applying a known technique to a known device (method or product) ready for improvement to yield predictable results.
Claim 12
Regarding claim 12, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, but does not teach the limitation wherein the computer-readable instructions further cause the one or more processors to filter the plurality of prototypes to eliminate one or more prototypes of the plurality of prototypes. However, Felfernig teaches this limitation (Felfernig, pg. 46, right col., first paragraph, “It is important to note that the application of the SDM [simple dominance model] happens after the recommender has filtered out completely unsuitable items”; Felfernig explains in the previous paragraph that these items are grouped with decoy items that increase their dominance value, and items not within a certain attribute or quality range are discarded).
It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Felfernig into that of Hsiao in view of Aggarwal. As the Hsiao combination does not include any form of filtering prototypes, Felfernig’s teachings can be added as an improvement. This would be applying a known technique to a known device (method or product) ready for improvement to yield predictable results. Furthermore, writing instructions into non-transitory memory to perform a filtering task is an obvious solution for a PHOSITA, which would be considered a combination of prior art elements.
Claim 13
Regarding claim 13, the rejection of claim 8 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the system of claim 8, but does not teach the limitation wherein the computer-readable instructions further cause the one or more processors to rank the plurality of prototypes. However, Felfernig teaches this limitation (Felfernig, pg. 43, right col., list item 3, “After the utility calculation of the products the system typically presents the top ranked products to the user”; the products are a combination of items and decoy items as explained on page 46).
It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Felfernig into that of Hsiao in view of Aggarwal. As the Hsiao combination does not include any form of ranking prototypes, Felfernig’s teachings can be added as an improvement. This would be applying a known technique to a known device (method or product) ready for improvement to yield predictable results. Furthermore, writing instructions into non-transitory memory to perform a ranking task is an obvious solution for a PHOSITA, which would be considered a combination of prior art elements.
Claim 19
Regarding claim 19, the rejection of claim 15 is incorporated. Further, the combination of Hsiao in view of Aggarwal teaches the computer-implemented method of claim 15, but does not teach the limitation further comprising filtering the plurality of prototypes to eliminate one or more prototypes of the plurality of prototypes. However, Felfernig teaches this limitation (Felfernig, pg. 46, right col., first paragraph, “It is important to note that the application of the SDM [simple dominance model] happens after the recommender has filtered out completely unsuitable items”; Felfernig explains in the previous paragraph that these items are grouped with decoy items that increase their dominance value, and items not within a certain attribute or quality range are discarded).
Felfernig is considered analogous to the claimed invention since they are in the same field of product and decoy design. It would have been obvious to a PHOSITA, before the effective filing date of the claimed invention, to incorporate the teachings of Felfernig into the teachings of Hsiao in view of Aggarwal. As the Hsiao combination does not include any form of filtering prototypes, Felfernig’s teachings can be added as an improvement. This would be applying a known technique to a known device (method or product) ready for improvement to yield predictable results.
Response to Arguments
The following is responsive to the remarks filed 26 May 2026.
35 U.S.C. 103
Applicants argue that Hsiao does not disclose “retrieving, product image data of currently existing products …” amended Claim 1 (last paragraph of page 8 in the remarks).
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Applicants’ arguments are moot because Hsiao teaches the limitation(s). See the current rejection(s) for detail mappings.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Burla et al. (U.S. 2021/0350036) disclose a system/method for computer aid design to facilitate manufacturing and structural performance.
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.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
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