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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/16/2025, 05/09/2026, 06/10/2025, and 04/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 112
1 The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
2 Claims 10-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
3 Claim 10 recites “…processing the normal map into a filter image and deforming the diffuse map using the filter image; and blending the deformed diffuse map and the filter image”. The claim notably refers to the usage of both the normal and diffuse maps. In claim 1 however, which claim 10 is dependent on, states “…generating at least one of a normal map or a diffuse map by feeding the mapping data into an artificial neural network (ANN) model…”, suggesting that the invention could utilize only one of the maps, but claim 10 mentions the use of both without mentioning the outcome of when only one of the maps is used. There is indefinite language for this limitation in the claim.
4 Claim 11 is dependent of claim 10, therefore it is also rejected under the same matter as claim 10.
Claim Rejections - 35 USC § 103
5 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.
6 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.
7 Claim(s) 1-4, 13, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1).
8 Regarding claim 1, Hauswiesner teaches a method of generating a texture map, comprising ([Abstract] reciting “In a method for producing three-dimensional model data of a garment having a target garment size, intermediate model data of the garment are stored, which are associated with a base garment size and include intermediate geometry data and at least one intermediate texture map associated with the intermediate geometry data.”):
receiving input data related to a target fabric from a user ([0069] reciting “In this step S10, the garment's intermediate geometry data D11 and its texture in the form of the intermediate texture maps D12, or only the intermediate texture maps D12, are input to a machine learning algorithm that assigns a semantic meaning to each element of the texture map, for example to each texel.”; [0089] reciting “Another reason for separating color information, respectively material information, and lighting information is that the same type of garment may often be produced in different colors or from different fabrics.”);
classifying the input data into mapping data for generating texture maps corresponding to information of the target fabric ([0022] reciting “The process can further be supported by providing information about the texture separately. For example, the separating is based on images of fabric patches or material patches associated with the intermediate model data.”);
generating at least one of or a diffuse map ([0022] reciting “For example, the fabric patches and/or material patches may be captured under white, diffuse and homogenous lighting conditions, which allows to compute the difference between the texture of the texture map, which includes the shading information, and the uniformly lit fabric sample or material sample.”) and;
generating a composite texture map corresponding to the target fabric based on the normal map or the diffuse map ([Abstract] reciting “Through semantic segmentation of the intermediate texture map a label map is generated that associates each element of the intermediate texture map to a respective one of a set of segments of the garment and associated resizing rules.”; [0111] reciting “Garments, however, are often partly or fully transparent. This means that some or all garment pixels in a rendered image consist of a mix of colors of the underlying layers of the garment, its back side and mannequin parts or background.”).
9 Hauswiesner does not explicitly teach generating at least one of a normal map or a diffuse map by feeding the mapping data into an artificial neural network (ANN) model;
10 Chao teaches generating at least one of a normal map or a diffuse map by feeding the mapping data into an artificial neural network (ANN) model ([Abstract] reciting “The computing apparatus inputs the fabric image to one of a plurality of neural network modules corresponding to different fabric classifications in the image processing module according to the fabric classification information to generate a normal map and a roughness map.”);
11 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner) to incorporate the teachings of Chao to provide a method that can include a normal map to go along with the diffuse maps that is provided by Hauswiesner to add into a specific type of neural network. Doing so would allow fabric digitalization information to have a rapidly and conveniently implemented process as stated by Chao ([0008] recited).
12 Regarding claim 2, Hauswiesner in view of Chao teaches the method of claim 1, wherein the receiving of the input data comprises (see claim 1 rejection above):
in response to receiving text data input from the user (Hauswiesner; [0137] reciting “User input devices 740 include all possible types of devices and mechanisms for inputting information to computer system 720…User input devices 740 typically allow a user to select objects, icons, text and the like that appear on the monitor 710 via a command such as a click of a button or the like.”), based on the text data.
13 Chao from claim 1 can further teach the limitations, specifically detecting information of the target fabric based on the text data ([0006] reciting “The computing apparatus inputs the fabric image to one of a plurality of neural network modules corresponding to different fabric classifications in the image processing module according to the fabric classification information to generate a normal map and a roughness map.”; [0024] reciting “In this embodiment, the color analysis module 3211 may analyze the fabric image 400 to generate fabric color information 301. To be specific, the color analysis module 3211 may generate an overall color histogram of the fabric image 400 and determines a color number in the fabric color information of the fabric image 400 according to at least one cluster peak of the overall color histogram.”).
14 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate additional teachings of Chao to provide a method that can detect types of information based on certain input, which the input can be text data provided by Hauswiesner in view of Chao. Doing so would allow fabric digitalization information to have a rapidly and conveniently implemented process as stated by Chao ([0008] recited).
15 Regarding claim 3, Hauswiesner in view of Chao teaches the method of claim 1, wherein the receiving of the input data comprises (see claim 1 rejection above):
in response to receiving image data input from the user, detecting information of the target fabric included in the image data (Hauswiesner; [0123] reciting “Furthermore, all smooth surfaces of the garment dressed on the mannequin are modified by adding stickers, tapes, structured light patterns or other visually distinctive features. The modified garment is captured in the same manner as the unmodified garment before. This results in two separate image sets D110, D111 that are both input to a separate 3D reconstruction step comprised by step G21.”).
16 Regarding claim 4, Hauswiesner in view of Chao teaches the method of claim 1, wherein the classifying of the input data into the mapping data comprises (see claim 1 rejection above):
classifying the information (Hauswiesner; [0022] reciting “The process can further be supported by providing information about the texture separately. For example, the separating is based on images of fabric patches or material patches associated with the intermediate model data.”) diffuse map information (Hauswiesner; [0022] reciting “For example, the fabric patches and/or material patches may be captured under white, diffuse and homogenous lighting conditions, which allows to compute the difference between the texture of the texture map, which includes the shading information, and the uniformly lit fabric sample or material sample.”)
, and the diffuse map information comprises information related to a fabric pattern representation (Hauswiesner; [0009] reciting “Prints on a garment usually also scale independently from the pattern of the fabric.”).
17 Chao from claim 1 can further teach the limitations, specifically classifying the information of the target fabric into normal map information, diffuse map information, or other information (“The computing apparatus integrates the fabric classification information, the normal map, and the roughness map to generate a fabric file.”),
wherein the normal map information comprises information related to a fabric type ([0026] reciting “…corresponding normal maps and roughness maps may be generated. For instance, if the fabric image 400 is determined to be cotton and linen fabric and is made through plain weaving, the image processing module 321 may input the fabric image 400 to the neural network module corresponding to the cotton and linen fabric and the plain weaving, so that this neural network module may correspondingly generate the normal map 304 and the roughness map 305 that may faithfully reflect properties of the corresponding physical fabric.”)…
18 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate additional teachings of Chao to provide a method that can classify information related to a normal map, as well as that normal map containing the type of fabric that can be obtained by Hauswiesner in view of Chao. Doing so would allow fabric digitalization information to have a rapidly and conveniently implemented process as stated by Chao ([0008] recited).
19 Regarding claim 13, Hauswiesner in view of Chao teaches the method of claim 1, further comprising (see claim 1 rejection above):
simulating the composite texture map onto a three-dimensional (3D) virtual garment, and displaying the 3D virtual garment to which the composite texture map is applied through a user interface (UI) (Hauswiesner; [0009] reciting “For example, the texture of the garment, e.g. representing the cloth, fabric and prints, is also not scaled uniformly but rather usually repeated. Therefore, knowledge of the used fabric can be used to simulate this behavior.”; [0094] reciting “If a garment has transparent parts or complex outlines like fringes, laces etc., alpha matting may be needed to realistically reproduce the garment's texture.”; [0132] reciting “In another application, the 3D model data can be used for virtual try-on of garments and mix and match applications. For example, the resulting garment model in the different sizes can be rigged to any new desirable body pose and fitted to a user's body through e.g. differential mesh editing. Such a fitted mesh enables ecommerce customers to better judge the fit and size of a garment and choose the proper convention size. Moreover, users can combine multiple garments together.”).
20 Claim 15 has similar limitations as of claim 1, therefore it is rejected under the same rationale as claim 1.
21 Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1) as of claim 1, further in view of Sunkavalli et al. (US 20190347526 A1).
22 Regarding claim 5, Hauswiesner in view of Chao teaches the method of claim 1, wherein the ANN model comprises (see claim 1 rejection above): but does not explicitly teach a generative ANN model configured to generate the texture maps as seamless maps.
23 Sunkavalli teaches a generative ANN model configured to generate the texture maps as seamless maps ([0023] reciting “The neural network encoder generates a latent representation (e.g., a feature map) of a digital image.”; [0137] reciting “FIG. 5 illustrates the digital image material property extraction system utilizing a digital image, captured by a mobile device with flash illumination, with the neural network components of the digital image material property extraction system and a densely connected continuous conditional random field to generate a model image that accurately portrays material properties captured from a digital image.”).
24 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate the teachings of Sunkavalli to provide a type of “seamless” or continuous type of maps from a generative neural network provided by Hauswiesner in view of Chao. Doing so would allow the methods to train and utilize a rendering layer to generate model images from the extracted material properties as stated by Sunkavalli ([Abstract] recited).
25 Regarding claim 6, Hauswiesner in view of Chao teaches the method of claim 1, wherein the ANN model comprises (see claim 1 rejection above): but does not explicitly teach a machine learning model configured to generate a preset by learning a correlation between a fabric type and the normal map.
26 Sunkavalli teaches a machine learning model configured to generate a preset by learning a correlation between a fabric type and the normal map ([0006] reciting “The deep-learning based framework then uses the neural network material property decoders to generate a material property set based on the extracted material classification and the feature map.”; [0076] reciting “As used herein, the term “normal” (sometimes referred to as a “normal parameter”) refers to surface directions portrayed in a digital image. In particular, the normal parameter includes a digital representation of surface normal directions corresponding to a plane of a surface of a material portrayed in a digital image. Furthermore, a normal can be represented as an angle between the surface direction and the plane of a surface of a material.”; [0086] reciting “For example, the digital image material property extraction system can utilize the one or more neural network material property decoders to generate a separate BRDF parameter predictions for each material type such as fabric, ground, leather, metal, stone-diffuse, stone-specular, polymer, and wood… Similarly, the neural network normal decoder and the neural network roughness decoder can also comprise K by N channels, in which K is the number of material types and N is the number output channels for BRDF parameters (i.e., normal values or roughness values).”).
27 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate the teachings of Sunkavalli to provide a method that includes some type of preset which are used in classifiers to figure out correlations from normal maps and fabric types that are provided by Hauswiesner in view of Chao. Doing so would allow the methods to train and utilize a rendering layer to generate model images from the extracted material properties as stated by Sunkavalli ([Abstract] recited).
28 Regarding claim 7, Hauswiesner in view of Chao and Sunkavalli teaches the method of claim 6, wherein the classifying of the input data into the mapping data comprises (see claims 1 and 6 rejections above):
29 Sunkavalli from claim 6 can further teach the limitations, specifically in response to the preset matching the input data ([0015] reciting “FIG. 4 illustrates a detailed process of jointly training a neural network encoder, a neural network material classifier, one or more neural network material property decoders, and a rendering layer of a digital image material property extraction system in accordance with one or more embodiments”), generating the mapping data based on the preset ([0006] reciting “The deep-learning based framework then uses the neural network material property decoders to generate a material property set based on the extracted material classification and the feature map.”).
30 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao and Sunkavalli) to incorporate additional teachings of Sunkavalli to provide a type of generation of the mapping date utilizing the preset taught by Hauswiesner in view of Chao and Sunkavalli. Doing so would allow the methods to train and utilize a rendering layer to generate model images from the extracted material properties as stated by Sunkavalli ([Abstract] recited).
31 Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1) and Sunkavalli et al. (US 20190347526 A1) as of claims 1 and 6, further in view of Tao et al. (US 20170132472 A1).
32 Regarding claim 8, Hauswiesner in view of Chao and Sunkavalli teaches the method of claim 6, wherein the classifying of the input data into the mapping data comprises (see claims 1 and 6 rejections above):
33 Sunkavalli from claim 6 can further teach most of the limitations, specifically …outputting mapping data related to the target fabric based on the input data ([0006] reciting “The deep-learning based framework then uses the neural network material property decoders to generate a material property set based on the extracted material classification and the feature map.”; [0020] reciting “FIG. 9 illustrates a flowchart of a series of acts for extracting material properties from input digital images in accordance with one or more embodiments”; [0038] reciting “In particular, a neural network decoder applies an algorithm (or set of algorithms) to a feature map to produce an output.”).
34 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao and Sunkavalli) to incorporate additional teachings of Sunkavalli to provide a method that can output map data based on inputs from Hauswiesner in view of Chao and Sunkavalli. Doing so would allow the methods to train and utilize a rendering layer to generate model images from the extracted material properties as stated by Sunkavalli ([Abstract] recited).
35 Although Hauswiesner in view of Chao and Sunkavalli could teach in response to the preset not matching the input data … (Sunkavalli; [0183] reciting “Basic-pt is the basic encoder-decoder network architecture without the neural network material classifier 108. Cls-pt adds the material classifier neural network material classifier 108, clsCRF-pt adds the material classifier and the DCRF, and clsOnly-pt is a conventional classification network.”), prior art from Tao can further teach the limitations.
36 Tao teaches in response to the preset not matching the input data … ([Abstract] reciting “In particular, in one or more embodiments, the disclosed systems and methods train the neural network encoder, the neural network material classifier, and one or more neural network material property decoders to accurately extract material properties from a single digital image portraying one or more materials.”; [0011] reciting “The processor(s) is(are) also configured to apply a generic mapping to the target object being tracked. The generic mapping is generated by learning possible appearance variations of a generic object. The processor(s) is(are) further configured to track the position of the target object in subsequent frames of the video sequence by determining whether an output of the generic mapping of the target object matches an output of the generic mapping of a candidate object.”).
37 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao and Sunkavalli) to incorporate the teachings of Tao to provide a clearer method when the type of preset does not match, which is a “generic” or default value, that can output various mapping data, utilizing the data provided by Hauswiesner in view of Chao and Sunkavalli. Doing so can utilize a rendering layer to generate model images from the extracted material properties as stated by Tao ([Abstract] recited).
38 Regarding claim 9, Hauswiesner in view of Chao, Sunkavalli, and Tao teaches the method of claim 8, wherein the outputting of the mapping data related to the target fabric comprises (see claims 1, 6, and 8 rejections above):
39 Tao from claim 8 can further teach the limitations, specifically in response to the mapping data not matching the preset and the mapping data being normal map information, outputting a candidate group of presets similar to the normal map information ([Abstract] reciting “The method also includes tracking the position of the target object in subsequent frames of the video sequence by determining whether an output of the generic mapping of the target object matches an output of the generic mapping of a candidate object.”; [0011] reciting “The processor(s) is(are) also configured to apply a generic mapping to the target object being tracked. The generic mapping is generated by learning possible appearance variations of a generic object. The processor(s) is(are) further configured to track the position of the target object in subsequent frames of the video sequence by determining whether an output of the generic mapping of the target object matches an output of the generic mapping of a candidate object.”; [0056] reciting “Normalization, which corresponds to whitening, may also be applied through lateral inhibition between neurons in the feature map.”).
40 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao, Sunkavalli, and Tao) to incorporate additional teachings of Tao to provide a method of when the type of preset does not match and using a type of normal maps, which is a “generic” or default value as previously mentioned by the teachings of Hauswiesner in view of Chao, Sunkavalli, and Tao, that can output a type of candidate objects/groups. Doing so can utilize a rendering layer to generate model images from the extracted material properties as stated by Tao ([Abstract] recited).
41 Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1) as of claims 1, further in view of Kim et al. (KR 20090012649 A).
42 Regarding claim 10, Hauswiesner in view of Chao teaches the method of claim 1, wherein the generating of the texture maps comprises (see claim 1 rejection above):
diffuse map (Hauswiesner; [0022] reciting “For example, the fabric patches and/or material patches may be captured under white, diffuse and homogenous lighting conditions, which allows to compute the difference between the texture of the texture map, which includes the shading information, and the uniformly lit fabric sample or material sample.”)
43 Hauswiesner in view of Chao does not explicitly teach processing the normal map into a filter image and deforming the diffuse map using the filter image; and blending the deformed diffuse map and the filter image.
44 Kim teaches processing the normal map into a filter image and deforming the diffuse map using the filter image; and blending the deformed diffuse map and the filter image ([Abstract] reciting “A method and a system for simultaneously scanning the shape and micro-structure of clothes are provided to scan the shape of clothes to be imaged and the micro-structure of the fabric of the clothes together without performing an additional post process…a scanning unit(100) having a plurality of lights(30) illuminating the clothes, and a computer combining the image data with a reflection coefficient map and a normal map to generate a 3D image with respect to the clothes, which includes the shape and micro-structure of the clothes.”; [Page 2, Paragraphs 12-13; Page 3, Paragraph 1] reciting “c) a method of extracting a reflection coefficient map imaged from a plurality of original microstructured image data of step b) in which the intrinsic color of the cloth constituting the cloth is removed, and the RGB values of the individual pixels are expressed as normal vectors. Extracting a normal map, which is an image of a weaving pattern of a cloth forming a garment; and d) combining the reflection coefficient map and the normal map of the step c) with the original shape image data of the step a) to generate a three-dimensional image of the garment in which the shape and the microstructure are combined.”; [Page 3; Paragraph 9] reciting “From a plurality of original microstructure image data obtained by photographing with the digital camera, a reflection coefficient map which imaged the intrinsic color of the cloth constituting the cloth while excluding the shadow, and a method of representing RGB values of individual pixels as normal vectors Image processing to extract the normal map image of the weaving pattern of the fabric forming the clothes”).
45 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate the teachings of Kim to provide a method that can provide a way to process and filter/extract a normal map for blending/combining, utilizing the diffuse maps from Hauswiesner in view of Chao in place of the coefficient maps that were also extracted and formed. Doing so would allow methods like simultaneously scanning the shape and microstructure of a garment as stated by Kim ([Page 3; Paragraph 1] recited).
46 Regarding claim 11, Hauswiesner in view of Chao and Kim teaches the method of claim 10, wherein the blending comprises (see claims 1 and 10 rejections above):
diffuse map (Hauswiesner; [0022] reciting “For example, the fabric patches and/or material patches may be captured under white, diffuse and homogenous lighting conditions, which allows to compute the difference between the texture of the texture map, which includes the shading information, and the uniformly lit fabric sample or material sample.”)
47 Kim as previously mentioned in claim 10 can further teach the limitations, specifically blending the filter image and the deformed diffuse map through image compositing ([Page 2, Paragraphs 13; Page 3, Paragraph 1] reciting “d) combining the reflection coefficient map and the normal map of the step c) with the original shape image data of the step a) to generate a three-dimensional image of the garment in which the shape and the microstructure are combined.”).
48 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao and Kim) to incorporate additional teachings of Kim to provide a type of image compositing method, which combines multiple images, for the blending methods that are provided by Hauswiesner in view of Chao and Kim. Doing so would allow methods like simultaneously scanning the shape and microstructure of a garment as stated by Kim ([Page 3; Paragraph 1] recited).
49 Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1) as of claims 1, further in view of Forutanpour et al. (US 20160335809 A1).
50 Regarding claim 12, Hauswiesner in view of Chao teaches the method of claim 1, further comprising (see claim 1 rejection above):
the diffuse map ([0022] reciting “For example, the fabric patches and/or material patches may be captured under white, diffuse and homogenous lighting conditions, which allows to compute the difference between the texture of the texture map, which includes the shading information, and the uniformly lit fabric sample or material sample.”)
51 Chao from claim 1 can further teach some of the limitations, specifically generating another map by feeding the mapping data, into the ANN model ([0026] reciting “The neural network modules 3213_1 to 3213_P may be trained separately by using a plurality of sample images corresponding to different fabric manufacturing methods and different fabric weaving methods in advance, so that the corresponding normal maps and roughness maps may be generated.”) wherein the generating of the composite texture map comprises:
generating a final composite texture map corresponding to the target fabric based on at least one of the normal map ([Abstract] reciting “The computing apparatus inputs the fabric image to one of a plurality of neural network modules corresponding to different fabric classifications in the image processing module according to the fabric classification information to generate a normal map and a roughness map.”)…
52 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate additional teachings of Chao to provide multiple maps (including a normal map as previously mention in claim 1) to feed the map data for the neural network provided by Hauswiesner in view of Chao. Doing so would allow fabric digitalization information to have a rapidly and conveniently implemented process as stated by Chao ([0008] recited).
53 Hauswiesner in view of Chao does not explicitly teach generating a final composite texture map corresponding to the target fabric based on at least one of the normal map, the diffuse map, or the other map, wherein the other map is generated based on normal map information and other information.
54 Forutanpour teaches generating a final composite texture map corresponding to the target fabric based on at least one of the normal map, the diffuse map, or the other map, wherein the other map is generated based on normal map information and other information. ([0101] reciting “The texture mapper, at 810, may use the camera seam error matte to avoid using pixels from the camera input image that may adversely contribute to the texture map pixel values.”; [0103] reciting “If there is another image frame to be processed, the other image frame may be processed, the texture pixels may be updated, and the composite confidence map may be updated based on the above-described techniques. If there is not another image frame to be processed, the processor may generate a “final” composite confidence map, at 818, based on the latest update. The final composite confidence map may include information (e.g., a “summary”) of all the pixels from each camera pose.”; [0104] reciting “The processor may use the final composite confidence map to identify texture holes and to identify any corresponding hole borders, at 820. Triangles of the 3D model corresponding to the identified texture holes may be rendered, at 822, and the triangles may be filled using the composite confidence map as an input channel (e.g., alpha), at 824.”).
55 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate the teachings of Forutanpour to provide a method that generates a final or last composite map based on the various map date, which can also include the normal and diffuse maps provided by Hauswiesner in view of Chao. Doing so would include information of all the pixels in the camera pose as stated by Forutanpour ([0103] recited).
56 Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hauswiesner et al. (US 20220189095 A1) in view of Chao et al. (US 20220292810 A1) as of claims 1, further in view of Azhand et al. (US 20220284652 A1).
57 Regarding claim 14, Hauswiesner in view of Chao teaches the method of claim 1, further comprising (see claim 1 rejection above): but does not explicitly teach outputting a feedback message to the user such that the user adjusts the input data in real time.
58 Azhand teaches outputting a feedback message to the user such that the user adjusts the input data in real time ([0022] reciting “The feedback provided to the user (second human) conveys to the user how to change the current pose with regard to the indicated body parts until the current distance for said body parts falls below the critical distance. In particular when using the modalities for feedback provisioning as text messages the user can quickly (in real-time) understand how to change the current pose by following the instructions with regard to a body part rather than looking into feedback which relates to particular single joints”).
59 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Hauswiesner in view of Chao) to incorporate the teachings of Azhand to provide a method that can output a type of feedback message to the user when the user real-time adjusts the input data provided by Hauswiesner in view of Chao (Hauswiesner; [0069] reciting “In this step S10, the garment's intermediate geometry data D11 and its texture in the form of the intermediate texture maps D12, or only the intermediate texture maps D12, are input to a machine learning algorithm that assigns a semantic meaning to each element of the texture map, for example to each texel.”; [0089] reciting “Another reason for separating color information, respectively material information, and lighting information is that the same type of garment may often be produced in different colors or from different fabrics.”). Doing so would be relevant to the correct performance of the specific exercise as stated by Azhand ([0022] recited).
Conclusion
60 Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY TRAN LE whose telephone number is (571)272-5680. The examiner can normally be reached Mon-Thu: 7:30am-5pm; First Fridays Off; Second Fridays: 7:30am-4pm.
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/JOHNNY T LE/Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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