DETAILED ACTION
Claims 1–16 are pending in the instant application.
This Office Action is in response Applicant’s argument filed on 05/27/2026.
THIS OFFICE ACTION IS MADE FINAL.
Response to Arguments
In re Applicant’s amendment, Examiner thanks Applicant expedited patent prosecution to overcome the 103 rejection. Following a search of the relevant art, a new ground(s) of rejection has been arisen in view of Choi et al. (U.S. 11,900,582 B2).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1–2, 11–13 and 15–16 are rejected under 35 U.S.C. § 103 as being unpatentable over Chao (U.S. 12,154,316 B2) in view of Choi (U.S. 11,900,582 B2).
Regarding claim 1, Chao discloses a method of estimating one or more physical property parameters of a target fabric, the method comprising:
receiving a two-dimensional (2D) image capturing a draped shape of the target fabric and basic information of the target fabric. (Per Fig. 1, Chao’s image capturing apparatus 110 receives a two-dimensional image of a fabric and analyzes its information. The image processing module 121 may automatically analyze the fabric image to obtain a plurality of pieces of corresponding fabric information. Chao col. 3 lines 8–33.)
However, Chao fails to specifically disclose estimating the one or more physical property parameters characterizing physical behavior of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and outputting the one or more physical property parameters characterizing physical behavior of the target fabric.
In related art, Choi discloses estimating the one or more physical property parameters characterizing physical behavior (Characterizing physical behavior construed as a 3D contour of the fabric visualized. See his col. 6 lines 42–46 and Fig. 5. [t]he possibility of training from the 3D contour of the fabric to the material property parameter may be verified through a correlation matric between the material property parameter and the 3D contour of the fabric visualized…) of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, (Per Fig. 5, Choi’s estimation apparatus discloses a trained model where a 2D image is used to reconstruct a 3D contour of the fabric. Choi col. 6 line 56 – col. 7 line 3. [t]he estimation device may use an artificial neural network that reconstructs a 3D contour shape from a top view 2D image of a fabric. The artificial neural network can be trained based on pair data of [top view 2D image−3D contour shape].) the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and (Per Fig. 6, Choi’s neural network discloses a material property parameter related to a stretch-weft stiffness of the fabric. Ibid. col. 9 lines 47–57. The first sub-neural network 610 may estimate a material property parameter related to a stiffness of fabric…)
outputting the one or more physical property parameters characterizing physical behavior of the target fabric. (Per Fig. 5, Choi’s neural network 610 outputs a related material parameters to analyze density of the fabric. Ibid. col. 10 lines 9–27. [i]n the material property parameters related to the stiffness of the fabric (“first group”) output by the first sub-neural network 610 and the material property parameters related to the bending of the fabric (“second group”) output by the second sub-neural network 630,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Choi into the teachings of Chao to analyze variations of a fabric when clothes appear worn. Ibid. col. 1 lines 19–23.
Regarding claim 15, Chao discloses a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to: receive a two-dimensional (2D) image capturing a draped shape of a target fabric and basic information of the target fabric. (Per Fig. 1, Chao’s image capturing apparatus 110 receives a two-dimensional image of a fabric and analyzes its information. The image processing module 121 may automatically analyze the fabric image to obtain a plurality of pieces of corresponding fabric information. Chao col. 3 lines 8–33.)
However, Chao fails to specifically disclose estimate the one or more physical property parameters characterizing physical behavior of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and output the one or more physical property parameters characterizing physical behavior of the target fabric.
In related art, Choi discloses estimate the one or more physical property parameters characterizing physical behavior (Characterizing physical behavior construed as a 3D contour of the fabric visualized. See his col. 6 lines 42–46 and Fig. 5. [t]he possibility of training from the 3D contour of the fabric to the material property parameter may be verified through a correlation matric between the material property parameter and the 3D contour of the fabric visualized…) of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, (Per Fig. 5, Choi’s estimation apparatus discloses a trained model where a 2D image is used to reconstruct a 3D contour of the fabric. Choi col. 6 line 56 – col. 7 line 3. [t]he estimation device may use an artificial neural network that reconstructs a 3D contour shape from a top view 2D image of a fabric. The artificial neural network can be trained based on pair data of [top view 2D image−3D contour shape].) the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and (Per Fig. 6, Choi’s neural network discloses a material property parameter related to a stretch-weft stiffness of the fabric. Ibid. col. 9 lines 47–57. The first sub-neural network 610 may estimate a material property parameter related to a stiffness of fabric…)
output the one or more physical property parameters characterizing physical behavior of the target fabric. (Per Fig. 5, Choi’s neural network 610 outputs a related material parameters to analyze density of the fabric. Ibid. col. 10 lines 9–27. [i]n the material property parameters related to the stiffness of the fabric (“first group”) output by the first sub-neural network 610 and the material property parameters related to the bending of the fabric (“second group”) output by the second sub-neural network 630,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Choi into the teachings of Chao to analyze variations of a fabric when clothes appear worn. Ibid. col. 1 lines 19–23.
Regarding claim 16, Chao discloses an apparatus for estimating one or more physical property parameters of a target fabric, the apparatus comprising:
a communication interface configured to receive a two-dimensional (2D) image capturing a draped shape of the target fabric and basic information of the target fabric. (Per Fig. 1, Chao’s image capturing apparatus 110 receives a two-dimensional image of a fabric and analyzes its information. The image processing module 121 may automatically analyze the fabric image to obtain a plurality of pieces of corresponding fabric information. Chao col. 3 lines 8–33.)
However, Chao fails to specifically disclose a processor configured to estimate the one or more physical property parameters characterizing physical behavior of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and output device configured to output the one or more physical property parameters characterizing physical behavior of the target fabric.
In related art, Choi discloses a processor configured to estimate the one or more physical property parameters characterizing physical behavior (Characterizing physical behavior construed as a 3D contour of the fabric visualized. See his col. 6 lines 42–46 and Fig. 5. [t]he possibility of training from the 3D contour of the fabric to the material property parameter may be verified through a correlation matric between the material property parameter and the 3D contour of the fabric visualized…) of the target fabric by applying the 2D image and the basic information of the target fabric to a neural network model, (Per Fig. 5, Choi’s estimation apparatus discloses a trained model where a 2D image is used to reconstruct a 3D contour of the fabric. Choi col. 6 line 56 – col. 7 line 3. [t]he estimation device may use an artificial neural network that reconstructs a 3D contour shape from a top view 2D image of a fabric. The artificial neural network can be trained based on pair data of [top view 2D image−3D contour shape].) the one or more physical property parameters comprising at least one of a stretching parameter of the target fabric or a bending parameter of the target fabric; and (Per Fig. 6, Choi’s neural network discloses a material property parameter related to a stretch-weft stiffness of the fabric. Ibid. col. 9 lines 47–57. The first sub-neural network 610 may estimate a material property parameter related to a stiffness of fabric…)
an output device configured to output the one or more physical property parameters characterizing physical behavior of the target fabric. (Per Fig. 5, Choi’s neural network 610 outputs a related material parameters to analyze density of the fabric. Ibid. col. 10 lines 9–27. [i]n the material property parameters related to the stiffness of the fabric (“first group”) output by the first sub-neural network 610 and the material property parameters related to the bending of the fabric (“second group”) output by the second sub-neural network 630,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Choi into the teachings of Chao to analyze variations of a fabric when clothes appear worn. Ibid. col. 1 lines 19–23.
Regarding claim 2, Chao as modified by Choi, discloses the method, wherein the basic information of the target fabric comprises: at least one of a type of the target fabric, composition of the target fabric, a density of the target fabric, a weight of the target fabric, a thickness of the target fabric, dyeing of the target fabric, and adding of printing to the target fabric. (Per Fig. 5, Choi’s neural network 610 outputs a related material parameters to analyze density of the fabric. Choi col. 10 lines 9–27. [i]n the material property parameters related to the stiffness of the fabric (“first group”) output by the first sub-neural network 610 and the material property parameters related to the bending of the fabric (“second group”) output by the second sub-neural network 630,)
Regarding claim 11, Chao as modified by Choi, the method, wherein the outputting the one or more physical property parameters of the target fabric comprises:
displaying a 3D drape simulation result corresponding to 3D clothes by applying the one or more physical property parameters to the target fabric used for the 3D clothes draped on an object. (Per Fig. 6, Chao discloses a 3D fabric model built by a normal map and a roughness map. Chao col. 8 line 54 – col. 9 line 8. [t]he three-dimensional model modeling software may perform three-dimensional model modeling according to the normal map and roughness map in each fabric file to generate a simulated three-dimensional fabric model.)
Regarding claim 12, Chao as modified by Choi, discloses the method, wherein the stretching parameter comprises at least one of a weft stretch force parameter, a warp stretch force parameter, or a shear parameter. (Per Fig. 5, Choi discloses stretch warp parameters. Choi col. 10 lines 9–27. The same group means that stretch properties have a strong correlation with other stretch properties (stretch weft/stretch warp/shear), and bend properties have a strong correlation with other bend properties (bend weft/bend warp/bend bias).)
Regarding claim 13, it has been rejected in the same manner as claim 12.
Claims 3–6, 10 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Chao in view of Choi and further in view of Wang (CN114925600A).
Regarding claim 3, Chao as modified by Choi, discloses the method, wherein the neural network model comprises at least one of: a regression model configured to estimate the stretching parameter related to stretching of the target fabric, based on the basic information of the target fabric. (Chao discloses a roughness map 305 in his generative network model discriminating different weaved fabric in order that features of a physical fabric are generated. Chao col. 7 lines 38–60. [t]he image processing module 321 provided by the disclosure may automatically generate a light and shadow feature (normal map 304) and a gray-scale feature (roughness map 305) that may faithfully reflect the features of the physical fabric.)
However, Chao as modified by Choi, fails to specifically disclose an estimation model trained to estimate the bending parameter related to bending of the target fabric, based on the latent vectors and the stretching parameter.
In related art, Wang discloses an estimation model trained to estimate the bending parameter related to bending of the target fabric, based on the latent vectors and the stretching parameter. (Wang discloses parameter vector from his trained VAE model to obtain bending stiffness. Wang para. ¶0066. [a]nd the bending stiffness of the real fabric to be measured is obtained using the learned deep neural network.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wang into the teachings of Chao and Choi to measure bending stiffness of draped fabrics. Id. para. ¶0007.
Regarding claim 4, it has been rejected in the same manner as claim 3.
Regarding claim 5, Chao as modified by Choi and Wang, discloses the method, wherein the estimating the stretching parameter comprises:
encoding the basic information of the target fabric into features; and (Per Fig. 1, Chao’s image capturing apparatus 110 receives a two-dimensional image of a fabric and analyzes its information. The image processing module 121 may automatically analyze the fabric image to obtain a plurality of pieces of corresponding fabric information. Chao col. 3 lines 8–33.)
estimating the stretching parameter by feeding the encoded features to the regression model. (Per Fig. 5, Choi’s estimation apparatus discloses a trained model where a 2D image is used to reconstruct a 3D contour of the fabric. Choi col. 6 line 56 – col. 7 line 3. [t]he estimation device may use an artificial neural network that reconstructs a 3D contour shape from a top view 2D image of a fabric. The artificial neural network can be trained based on pair data of [top view 2D image−3D contour shape].)
Regarding claim 6, Chao as modified by Choi and Wang, discloses the method, wherein the encoding comprises encoding the features indicating a type of the target fabric among the basic information of the target fabric. (Per Fig. 1, Chao’s image capturing apparatus 110 receives a two-dimensional image of a fabric and analyzes its information. The image processing module 121 may automatically analyze the fabric image to obtain a plurality of pieces of corresponding fabric information. Chao col. 3 lines 8–33.)
Regarding claim 10, Chao as modified by Choi, discloses the claimed invention, but fails to specifically disclose the method, wherein the stretching parameter correlates with the basic information of the fabric, and the bending parameter correlates with the draped shape of the target fabric.
In related art, Wang discloses the method, wherein the stretching parameter correlates with the basic information of the fabric, and the bending parameter correlates with the draped shape of the target fabric. (Per Fig. 3, Wang discloses a bending stiffness of draped fabrics. Wang para. ¶0065. [a] learning-based method for measuring the bending stiffness of draped
fabrics includes:)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wang into the teachings of Chao and Choi to measure bending stiffness of draped fabrics. Id. para. ¶0007.
Regarding claim 14, Chao as modified by Choi, discloses the claimed invention, but fails to specifically disclose the method, wherein the 2D image comprises a top view image capturing the draped shape of a circular specimen of the target fabric.
In related art, Wang discloses the method, wherein the 2D image comprises a top view image capturing the draped shape of a circular specimen of the target fabric. (Wang discloses a multi-view map simulating fabric dataset. Wang para. ¶0066. [a] multi-view depth map is generated using the simulation dataset;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wang into the teachings of Chao and Choi to measure bending stiffness of draped fabrics. Id. para. ¶0007.
Allowable Subject Matter
Claims 7–9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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.
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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