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 .
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 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.
Obligation Under 37 CFR 1.56 – Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Response to Amendment
Applicant’s amendment filed on June 13, 2026 has been entered. Claims 1-2, 5, 7, 21, 23 and 25-26 have been amended. Claims 27-30 have been canceled. No claims have been added. Thus, claims 1-26 are still pending in this application, with claims 1, 25 and 26 being independent.
Claim Objections
Claim 19 is objected to because of the following informalities: “at a predicted arrangement points” (line 3 of claim 1, emphasis added) is grammatically improper since “a” is a singular article and “points” is a plural noun. Replacing “points” with “point” would cure the deficiency. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 7 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. In particular, claim 7 has been amended to now recite, “positions of the predefined points on the 3D avatar” (line 4 of claim 7). However, there is no support for “predefined points on the 3D avatar” since, as recited in claim 1 and supported by the specification, the predefined points comprise points on arrangement plates surrounding the 3D avatar, not on the avatar.
Claim Rejections - 35 USC § 112(b)
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.
Claims 7 and 17-19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 7, claim 7 has been amended to now recite, “positions of the predefined points on the 3D avatar” (line 4 of claim 7). However, it is unclear how the predefined points can be on the 3D avatar since, as recited in claim 1 and supported by the specification, the predefined points comprise points on arrangement plates surrounding the 3D avatar, not on the avatar.
Claim 17 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
“at arrangement points that are symmetrically placed on the 3D avatar.” (lines 6-7 of claim 17)
Regarding claim 17, claim 17 recites, “at arrangement points that are symmetrically placed on the 3D avatar” (lines 6-7 of claim 17). However, claim 1 recites “arrangement points” (line 5), and it is unclear whether “arrangement points” recited in claim 17 refers to the “arrangement points” recited in claim 1, or to other arrangement point (on the avatar, not on the arrangement plates.. Thus, claim 17 is vague and indefinite for at least this reason.
Furthermore, it is also unclear how the arrangement points can be on the 3D avatar since, as recited in claim 1 and supported by the specification, the arrangement points comprise predefined points on arrangement plates surrounding the 3D avatar, not on the avatar. Thus, claim 17 is also indefinite for this reason.
Regarding claim 18, claim 18 recites, “the predicted arrangement point on the 3D avatar” (line 4 of claim 18). However, it is unclear how the predicted arrangement point can be on the avatar since, as recited in claim 1 and supported by the specification, the arrangement points comprise predefined points on arrangement plates surrounding the 3D avatar, not on the avatar. Thus, claim 18 is indefinite for this reason and also by virtue of its dependence on indefinite claim 17.
Regarding claims 19 and 20, claims 19 and 20 depend on indefinite claim 17 and, as such, are likewise indefinite at least by virtue of their dependence on indefinite claim 17.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art;
Ascertaining the differences between the prior art and the claims at issue;
Resolving the level of ordinary skill in the pertinent art; and
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6-8 and 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over FRAKES et al. (US 2020/0027155, hereinafter “FRAKES”) in view of GOLDBERG et al. (US 2011/0245633, hereinafter “GOLDBERG”), and further in view of JU (US 2021/0056754).
Regarding claim 26, FRAKES discloses a computing device (e.g., ¶ [0067]: “computing device 101 for visualizing a garment fit”) comprising:
one or more processors (e.g., ¶ [0067]: “computing device 101 can include at least one of: processor(s) 110, memory 120, body pre-processor 130, garment pre-processor 140, finite element solver 150, soft-body dynamics solver 160, user interface component(s) 170, and communication component(s) 180.” ¶ [0068]: “The one or more processors 110 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected.”); and
memory (e.g., ¶ [0069]: “The memory 120 can store data 121 and instructions 122 which are executed by the processor 110 to cause computing device 101 to perform operations.”) storing instructions thereon, the instructions when executed by the one or more processors (e.g., ¶ [0069]: “The memory 120 can store data 121 and instructions 122 which are executed by the processor 110 to cause computing device 101 to perform operations.” ¶ [0081]: “The method(s) can be implemented by one or more computing devices, such as the computing device 101 depicted in FIG. 1.”) cause the one or more processors to:
receive pattern information (e.g., ¶ [0034]: “obtain data indicative of a shape of a garment panel (e.g., from a garment model file)”) indicating configurations of each of patterns of a garment (e.g., ¶ [0034]: “shape of a garment panel”) (¶ [0011]: “obtaining, by one or more computing devices, garment data descriptive of a garment and body data descriptive of a body.” ¶ [0028]: “the one or more garment panels can be stored as a two-dimensional (2D) cutting pattern, a 2D mesh file, a 3D mesh file, or any other suitable format.” ¶ [0029]: “The garment panel(s) can correspond to cutting patterns for the garment. The garment panel(s) can be represented within the garment model file, for example, as blocks or pieces. Each of the garment panel(s) can also have or otherwise be associated with one or more garment feature(s). The garment feature(s) can include, for example, a dart, pocket, placket, j-curve, yolk, dart, embroidery, buttons, etc. The garment feature(s) can be represented within the garment model file, for example, as lines or patterns in one or more layers of each block or piece.” ¶ [0034]: “The garment pre-processor can obtain data indicative of a shape of a garment panel (e.g., from a garment model file), and input the data into the pattern recognition algorithm.” ¶ [0073]: “obtain the one or more garment panels from the memory 120.” ¶ [0110]: “At (601), the method 600 can include obtaining data indicative of a garment model. The data indicative of the garment model can include, for example, garment panels, garment features, and garment material properties. As an example, memory 120 can include one or more garment panels and a garment materials database. The one or more garment panels can include one or more garment features associated with the garment panels. The garment materials database can include one or more garment material models, one or more textile mechanical properties corresponding to each garment material model, and one or more textures corresponding to each garment material model. The garment pre-processor 140 can obtain the one or more garment panels, and one or more associated garment features from the memory 120, and obtain garment material properties corresponding to the garment panels from the garment materials database in the memory 120.” ¶ [0116]: “The garment panels 910 can be obtained, for example, by the garment pre-processor 140 from memory 120.” ),
apply the pattern information (e.g., ¶ [0034]: “obtain data indicative of a shape of a garment panel (e.g., from a garment model file), and input the data into the pattern recognition algorithm.”) to a neural network model (e.g., ¶ [0034]: “The pattern recognition algorithm can include, for example, a classification tree, a machine-learned pattern recognition model,” NOTE: One of ordinary skill in the art would understand that a neural network model is merely one well-known type of “machine-learned pattern recognition model”. Thus, as a simple design choice for implementing the machine-learned pattern recognition model disclosed in FRAKES, it would have been obvious to one of ordinary skill in the art at the time of the invention to have used a neural network model as one well-known implementation for a machine-learned pattern recognition model. For instance, GOLDBERG et al. (US 2011/0245633) clearly teaches that pattern recognition algorithms include machine learning algorithms such as neural networks (See ¶ [0014] of GOLDBERG: “pattern recognition algorithms include machine learning algorithms such as Dynamic Baysian Networks, neural networks, conditional random fields, hidden Markov models, Kalman filters, fuzzy logic, kernel estimations, k-nearest neighbor, learning vector quantization, Gaussian models, and/or radial basis function.”).) to extract features from the configurations of each of the patterns (e.g., ¶ [0034]: “recognize the garment panel shape and/or the one or more geometric feature(s) of the garment panel,” ¶ [0034]: “The pattern recognition algorithm can output data indicative of a body landmark classification of the garment panel and/or data indicative of one or more geometric feature(s) of each garment panel.”) (¶ [0032]: “the garment pre-processor can parse a garment model file (e.g., CAD file) to identify one or more garment panel(s), and classify each of the identified garment panel(s). The garment pre-processor can classify the identified garment panel(s) according to one or more body landmark(s). Each of the body landmark(s) can be associated with a specific region or location on a 3D body model. A body landmark can be associated with, for example, a front, back, chest, abdomen, seat, left/right arm, left/right leg, waist, neck, shoulders, left/right elbow, left/right knee, left/right wrist, etc. on a 3D body model.” ¶ [0032]: “the garment pre-processor can classify the garment panel(s) based on a garment type and/or a garment panel shape.” ¶ [0034]: “the garment pre-processor can use a pattern recognition algorithm to classify each panel in the garment model file. For example, the garment pre-processor can use a pattern recognition algorithm to determine a body landmark classification for a garment panel. The garment pre-processor can obtain data indicative of a shape of a garment panel (e.g., from a garment model file), and input the data into the pattern recognition algorithm. The pattern recognition algorithm can output data indicative of a body landmark classification of the garment panel and/or data indicative of one or more geometric feature(s) of each garment panel. The pattern recognition algorithm can include, for example, a classification tree, a machine-learned pattern recognition model, and/or a probability/score calculation approach to help recognize the garment panel shape and/or the one or more geometric feature(s) of the garment panel, and to determine the body landmark classification of the garment panel. The machine learned approach and the probability/score calculation approach can use any unique identifying features of the garment panel to match it to a garment panel from a garment panel database.” ¶ [0034]: “for each garment panel in the garment model file” ¶ [0035]: “subsequent to classifying the garment panel, the garment pre-processor can identify one or more garment feature(s) associated with a garment panel in a garment model file. For example, the garment pre-processor can use a probability lookup table, machine-learned feature recognition model, and/or other pattern recognition algorithms to identify the garment feature(s) based on lines or patterns in one or more layers of one or more blocks or pieces in the garment model file. The pattern recognition algorithm for identifying the one or more garment feature(s) can be similar to the pattern recognition algorithm to classify each panel in the garment model file described above.” ¶ [0073]: “The garment pre-processor 140 can obtain the one or more garment panels from the memory 120. The garment pre-processor 140 can determine one or more stitch lines/curves and one or more respective attachment points for each of the one or more garment panels.”),
predict arrangement points (e.g., ¶ [0034]: “determine the body landmark classification of the garment panel”), by the neural network model (e.g., ¶ [0034]: “machine-learned pattern recognition model”), features (e.g., ¶ [0034]: “recognize the garment panel shape and/or the one or more geometric feature(s) of the garment panel, and to determine the body landmark classification of the garment panel.”) (¶ [0032]: “the garment pre-processor can parse a garment model file (e.g., CAD file) to identify one or more garment panel(s), and classify each of the identified garment panel(s). The garment pre-processor can classify the identified garment panel(s) according to one or more body landmark(s). Each of the body landmark(s) can be associated with a specific region or location on a 3D body model. A body landmark can be associated with, for example, a front, back, chest, abdomen, seat, left/right arm, left/right leg, waist, neck, shoulders, left/right elbow, left/right knee, left/right wrist, etc. on a 3D body model.” ¶ [0034]: “the garment pre-processor can use a pattern recognition algorithm to classify each panel in the garment model file. For example, the garment pre-processor can use a pattern recognition algorithm to determine a body landmark classification for a garment panel. The garment pre-processor can obtain data indicative of a shape of a garment panel (e.g., from a garment model file), and input the data into the pattern recognition algorithm. The pattern recognition algorithm can output data indicative of a body landmark classification of the garment panel and/or data indicative of one or more geometric feature(s) of each garment panel. The pattern recognition algorithm can include, for example, a classification tree, a machine-learned pattern recognition model, and/or a probability/score calculation approach to help recognize the garment panel shape and/or the one or more geometric feature(s) of the garment panel, and to determine the body landmark classification of the garment panel. The machine learned approach and the probability/score calculation approach can use any unique identifying features of the garment panel to match it to a garment panel from a garment panel database.” ¶ [0112]: “At (603), the method 600 can include positioning the garment panels onto the body model. For example, the garment pre-processor 140 can classify each of the garment panels according to one or more body landmarks, and match the garment panels with the one or more predetermined body landmarks associated with the body model, in order to position the garment panels on the body model. In addition, the garment pre-processor 140 can use a rules based approach to fine-tune a location at which to position the garment panels on the body model.” ¶ [0116]: “The garment pre-processor 140 can classify the garment panels 910 according to body landmarks. In particular, the garment panels 910 can be classified according to the body landmarks: left arm, right arm, front left side torso, front right side torso, back left side, and back right side.” ),
arrange at least a subset of the patterns at the predicted arrangement points (¶ [0036]: “the garment pre-processor can position the identified garment panel(s) on a 3D body model. The garment pre-processor can position the identified garment panel(s) based on the body landmark classification of the garment panel(s). The garment pre-processor can receive a 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model.” ¶ [0036]: “The garment pre-processor can position and hold the garment panel(s) at a specific region or location on the 3D body model by matching the body landmark classification of the garment panel(s) with the predetermined body landmark(s) associated with the 3D body model, so that the garment panel(s) can be stitched to create a 3D garment model representing the garment. For example, if a garment panel is for a chest body landmark, then the garment pre-processor can position the garment panel at a region or location on a 3D body model that is associated with the chest body landmark (e.g., the chest region on the 3D body model).” ¶ [0112]: “At (603), the method 600 can include positioning the garment panels onto the body model. For example, the garment pre-processor 140 can classify each of the garment panels according to one or more body landmarks, and match the garment panels with the one or more predetermined body landmarks associated with the body model, in order to position the garment panels on the body model. In addition, the garment pre-processor 140 can use a rules based approach to fine-tune a location at which to position the garment panels on the body model.” ¶ [0116]: “The garment pre-processor 140 can position the garment panels 910 by matching the garment panels with the one or more predetermined body landmarks associated with the body model 901, based on the classification of the garment panels 910.”),
assemble the patterns from the arrangement points into the garment (e.g., ¶ [0036]: “position and hold the garment panel(s) at a specific region or location on the 3D body model by matching the body landmark classification of the garment panel(s) with the predetermined body landmark(s) associated with the 3D body model, so that the garment panel(s) can be stitched to create a 3D garment model representing the garment.”) placed on the 3D avatar (e.g., ¶ [0036]: “position and hold the garment panel(s) at a specific region or location on the 3D body model “ ¶ [0027]: “three dimensional model (3D) of a body” ¶ [0071]: “a body model representing a target body”) (¶ [0027]: “computational models representing garment cutting patterns can be stitched directly on a three dimensional model (3D) of a body to create a 3D garment model representing the garment. Stitching can be performed by stitching different edges of a garment cutting pattern according to a specific sequence.” ¶ [0028]: “the garment pre-processor can generate a model of a garment from one or more garment panels associated with such garment. For example, the one or more garment panels can be stored as a two-dimensional (2D) cutting pattern, a 2D mesh file, a 3D mesh file, or any other suitable format. One or more stitch lines/curves and one or more respective attachment points can be determined for each of the one or more garment panels. A finite element solver can be used to perform stitching by connecting the one or more stitch lines/curves in 3D along the one or more respective attachment points to create a 3D stitched garment. The finite element solver used to perform stitching can be the same as or different from the finite element solver used to perform garment deformation. The finite element solver can perform the stitching of the garment with or without a 3D body model between garment components.” ¶ [0036]: “The garment pre-processor can position and hold the garment panel(s) at a specific region or location on the 3D body model by matching the body landmark classification of the garment panel(s) with the predetermined body landmark(s) associated with the 3D body model, so that the garment panel(s) can be stitched to create a 3D garment model representing the garment. For example, if a garment panel is for a chest body landmark, then the garment pre-processor can position the garment panel at a region or location on a 3D body model that is associated with the chest body landmark (e.g., the chest region on the 3D body model).” ¶ [0073]: “the garment pre-processor 140 can prepare the garment model by stitching one or more garment panels along one or more stitch lines or curves. For example, the garment pre-processor 140 can generate a garment model of a garment based at least in part on one or more garment panels associated with the garment. The garment pre-processor 140 can obtain the one or more garment panels from the memory 120. The garment pre-processor 140 can determine one or more stitch lines/curves and one or more respective attachment points for each of the one or more garment panels. In some implementations, a finite element solver can be used to perform stitching by connecting the one or more stitch lines/curves in 3D along the one or more respective attachment points to create a 3D stitched garment.” ¶ [0073]: “the garment pre-processor 140 can perform the stitching with a body model, so that a garment stitching approach can stitch garment cutting patterns directly on the body model.” ¶ [0113]: “At (604), the method 600 can include stitching the garment panels to prepare the garment model. For example, the garment pre-processor 140 can analyze the garment panels, and determine a stitching sequence based on a size, position, material properties, or garment features associated with the garment panels. The stitching sequence can include, for example, a stitching order and/or different stitching techniques for stitching the garment panels along one or more stitch lines/curves and one or more attachment points. The garment pre-processor 140 can stitch each of the garment panels along the one or more stitch lines/curves and one or more respective attachment points.”), and
perform simulation of the garment on the 3D avatar (¶ [0011]: “simulating, by the one or more computing devices, a garment deformation of the garment due to contact from the body.” ¶ [0012]: “simulate deformation of the garment on the body.” ¶ [0013]: “simulate deformation of the garment due to contact with the body model” ¶ [0026]: “a modified finite element solver can be used to simulate deformation of the garment due to contact from the body while a soft-body solver can be used to simulate deformation of the body due to contact from the garment.” ¶ [0074]: “The finite element solver 150 can be configured to simulate deformation of a garment on a body.” ¶ [0086]: “At (204), the method 200 can include simulating garment deformation.” ¶ [0087]: “At (205), the method 200 can include simulating body deformation.” ¶ [0088]: “At (206), the method 200 can include visualizing garment fit and appearance.” ¶ [0116]: “The garment pre-processor 140 can determine a stitching sequence 911, and perform stitching by connecting the garment panels 910 according to the stitching sequence 911 in 3D. The garment pre-processor 140 can perform the stitching of the garment with or without the body model 901 between garment panels.”).
FRAKES fails to disclose predicting arrangement points: “from a plurality of predefined points on arrangement plates surrounding a three-dimensional (3D) avatar.”
However, whereas FRAKES is not explicit as to, JU teaches:
arrangement points (e.g., ¶ [0006]: “arrangement points”), from a plurality of predefined points (e.g., ¶ [0004]: “arrangement points may be automatically generated by analyzing the shape of the 3D avatar”) on arrangement plates (e.g., ¶ [0006]: “bounding volumes”) surrounding a three-dimensional (3D) avatar (e.g., ¶ [0004]: “a three-dimensional (3D) avatar”) (¶ [0004]: “bounding volumes matched with the size and shape of a three-dimensional (3D) avatar, bounding volumes and arrangement points may be automatically generated by analyzing the shape of the 3D avatar” ¶ [0006]: “arrangement points may be are automatically placed on the bounding volumes to determine the locations of the clothing patterns depending on the attributes of the bounding volumes for each body part, to which the body type and orientation of the 3D avatar are reflected;”), for placing the patterns relative to the 3D avatar on which the garment is placed by processing the extracted features (e.g., ¶ [0006]: “arrangement points may be are automatically placed on the bounding volumes to determine the locations of the clothing patterns depending on the attributes of the bounding volumes for each body part, to which the body type and orientation of the 3D avatar are reflected;” ¶ [0007]: “extracting feature points from data of a three-dimensional (3D) avatar, determining attributes of bounding volumes for each body part, to which a body type and an orientation of the 3D avatar are reflected, based on locations of the feature points, placing arrangement points on the bounding volumes, depending on the attributes of the bounding volumes for each body part, and determining initial locations of clothing patterns, based on the arrangement points.” ¶ [0018]: “a method of displaying clothing patterns includes determining attributes of bounding volumes for each body part, to which a body type and an orientation of a 3D avatar are reflected, based on locations of feature points extracted from data of the 3D avatar, determining initial locations of the clothing patterns by placing arrangement points on the bounding volumes depending on the attributes of the bounding volumes for each body part, and draping the clothing patterns to the 3D avatar depending on the initial locations.” ¶ [0047]: “In operation 130, the display apparatus places arrangement points on the bounding volumes depending on the attributes of the bounding volumes for each body part. The display apparatus may generate and place the arrangement points on the bounding volumes depending on the attributes of the bounding volumes for each body part. The ‘arrangement points’ refer to candidate locations on a 3D avatar for initially placing 2D clothing patterns. For each bounding volume, a grid may be used to place the bounding volume. The number and/or sparsity of grid points may differ depending on bounding volumes associated with the grid. For example, a grid for the bound volume of a torso may be denser than that for the bound volume of an arm.” ¶ [0104]: “The display apparatus may display bounding volumes in which arrangement points are placed depending on the attributes of bounding volumes for each body part.” ¶ [0107]: “The processor 930 determines the initial locations of the clothing patterns by placing arrangement points on the bounding volumes depending on the attributes of the bounding volumes for each body part. The processor 930 drapes the clothing patterns to the 3D avatar depending on the initial locations.”).
Thus, in order to obtain a more versatile system having the cumulative features and/or functionalities taught by FRAKES and JU, it would have been obvious to one of ordinary skill in the art to have modified the system taught by FRAKES so as to predict arrangement points from a plurality of predefined points on arrangement plates surrounding a three-dimensional (3D) avatar, as taught by JU.
Regarding claim 25, claim 25 is directed to a non-transitory computer-readable storage medium storing instructions that when executed by one or more processors cause the processors to implement the method implemented by the computing device of claim 26 and, as such, is rejected for the same reasons applied above in the rejection of claim 26.
Regarding claim 1, claim 1 is directed to the method implemented by the computing device of claim 26 and, as such, is rejected for the same reasons applied above in the rejection of claim 26.
Regarding claim 6 (depends on claim 1), FRAKES discloses that the pattern information further comprises at least one of:
symmetry information of the patterns indicating which of the patterns are symmetrical (This limitation need does not need to be met since at least one of the alternative limitations has been met.);
a total number of the patterns in the garment (¶ [0033]: “As an example, if a garment model file includes a garment model of a shirt type garment, then the garment pre-processor can determine that the garment model file should include at least one garment panel for each of the body landmarks: chest and abdomen, back, left arm, right arm, and shoulders. If the garment model file includes a dress type garment, then the garment pre-processor can determine that the garment model file should include at least one garment panel for each of the body landmarks: front, and back.” ); and
internal line segment information of the patterns comprising at least one of a notch of the patterns, a sewing line of the patterns, a cut line of the patterns, a dart line of the patterns, a length of each line segment of the patterns, or a curvature of each line segment of the patterns (¶ [0029]: “In some implementations, a 3D garment model can be represented by or generated from or based on a computer-aided design (CAD) model file (e.g., .DXF file). The garment model file can include data representing a garment type and one or more garment panel(s). The garment type can correspond to a general classification of a garment, such as, for example, a shirt, pants, top, dress, etc. The garment panel(s) can correspond to cutting patterns for the garment. The garment panel(s) can be represented within the garment model file, for example, as blocks or pieces. Each of the garment panel(s) can also have or otherwise be associated with one or more garment feature(s). The garment feature(s) can include, for example, a dart, pocket, placket, j-curve, yolk, dart, embroidery, buttons, etc. The garment feature(s) can be represented within the garment model file, for example, as lines or patterns in one or more layers of each block or piece.” ¶ [0035]: “In some implementations, optionally subsequent to classifying the garment panel, the garment pre-processor can identify one or more garment feature(s) associated with a garment panel in a garment model file. For example, the garment pre-processor can use a probability lookup table, machine-learned feature recognition model, and/or other pattern recognition algorithms to identify the garment feature(s) based on lines or patterns in one or more layers of one or more blocks or pieces in the garment model file.” ¶ [0028]: “In some implementations, the garment pre-processor can generate a model of a garment from one or more garment panels associated with such garment. For example, the one or more garment panels can be stored as a two-dimensional (2D) cutting pattern, a 2D mesh file, a 3D mesh file, or any other suitable format. One or more stitch lines/curves and one or more respective attachment points can be determined for each of the one or more garment panels. A finite element solver can be used to perform stitching by connecting the one or more stitch lines/curves in 3D along the one or more respective attachment points to create a 3D stitched garment.” ¶ [0040]: “The stitching sequence can include, for example, a stitching order and/or different stitching techniques for stitching the garment panel(s) along one or more stitch lines/curves and one or more attachment points for the garment panel(s).” ¶ [0073]: “The garment pre-processor 140 can obtain the one or more garment panels from the memory 120. The garment pre-processor 140 can determine one or more stitch lines/curves and one or more respective attachment points for each of the one or more garment panels.”).
Regarding claim 7 (depends on claim 1), FRAKES discloses:
receiving supplemental information comprising at least one of a size of the 3D avatar (¶ [0038]: “As an example, if a garment panel is for a right arm body landmark, then the garment pre-processor can position a midpoint of the garment panel at an elbow location within a right arm region on the 3D body model. In addition, the garment pre-processor can position a should end of the garment panel at a shoulder location within the right arm region on the 3D body model. In addition, before positioning the midpoint, the garment pre-processor can determine if a length of the garment panel is sufficient for the garment panel to extend from the shoulder location to the elbow location.” NOTE: In order to determine if a length of the garment panel is sufficient to extend from the shoulder location to the elbow location, the size of the 3D body model (e.g., the length from the shoulder to the elbow) must be known. Thus, a size of the 3D body model must be received.), positions of the predefined points on the 3D avatar (e.g., ¶ [0036]: “The garment pre-processor can receive a 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model.” ¶ [0112]: “the one or more predetermined body landmarks associated with the body model,”), or a size of an arrangement plate comprising the predefined points (This limitation need does not need to be met since at least one of the alternative limitations has been met.) (¶ [0032]: “In some implementations, the garment pre-processor can parse a garment model file (e.g., CAD file) to identify one or more garment panel(s), and classify each of the identified garment panel(s). The garment pre-processor can classify the identified garment panel(s) according to one or more body landmark(s). Each of the body landmark(s) can be associated with a specific region or location on a 3D body model. A body landmark can be associated with, for example, a front, back, chest, abdomen, seat, left/right arm, left/right leg, waist, neck, shoulders, left/right elbow, left/right knee, left/right wrist, etc. on a 3D body model. In some implementations, the garment model file can include data representing a body landmark classification for one or more garment panel(s), and the garment pre-processor classify the garment panel(s) based on the body landmark classification information. In some implementations, the garment pre-processor can classify the garment panel(s) based on a garment type and/or a garment panel shape.” ¶ [0033]: “As an example, if a garment model file includes a garment model of a shirt type garment, then the garment pre-processor can determine that the garment model file should include at least one garment panel for each of the body landmarks: chest and abdomen, back, left arm, right arm, and shoulders.” ¶ [0036]: “In some implementations, the garment pre-processor can position the identified garment panel(s) on a 3D body model. The garment pre-processor can position the identified garment panel(s) based on the body landmark classification of the garment panel(s). The garment pre-processor can receive a 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model. Alternatively, the garment pre-processor can obtain a template 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model. The garment pre-processor can position and hold the garment panel(s) at a specific region or location on the 3D body model by matching the body landmark classification of the garment panel(s) with the predetermined body landmark(s) associated with the 3D body model, so that the garment panel(s) can be stitched to create a 3D garment model representing the garment. For example, if a garment panel is for a chest body landmark, then the garment pre-processor can position the garment panel at a region or location on a 3D body model that is associated with the chest body landmark (e.g., the chest region on the 3D body model).” ¶ [0037]: “In some implementations, the garment pre-processor can use a rules based approach to position the garment panel(s) on the 3D body model. In particular, the garment pre-processor can use the rules based approach (e.g., a garment pre-processing template) to fine-tune a location within a region on the 3D body model, and determine a segment or point within a garment panel that corresponds to the location. The garment pre-processor can position the segment or point within the garment panel at the location within the region on the 3D body model. The rules based approach can be based on a predetermined set of rules obtained from the garment pre-processing template.” ¶ [0038]: “As an example, if a garment panel is for a right arm body landmark, then the garment pre-processor can position a midpoint of the garment panel at an elbow location within a right arm region on the 3D body model. In addition, the garment pre-processor can position a should end of the garment panel at a shoulder location within the right arm region on the 3D body model. In addition, before positioning the midpoint, the garment pre-processor can determine if a length of the garment panel is sufficient for the garment panel to extend from the shoulder location to the elbow location.” ¶ [0071]: “In some implementations, the body pre-processor 130 can skip compression. For example, the body pre-processor 130 can select a body model representing a template body (e.g., for use by a body morphing approach), or select a body model representing a target body (e.g., for use by a garment stitching approach).” ¶ [0112]: “At (603), the method 600 can include positioning the garment panels onto the body model. For example, the garment pre-processor 140 can classify each of the garment panels according to one or more body landmarks, and match the garment panels with the one or more predetermined body landmarks associated with the body model, in order to position the garment panels on the body model. In addition, the garment pre-processor 140 can use a rules based approach to fine-tune a location at which to position the garment panels on the body model.” ¶ [0116]: “FIG. 9 depicts an example graphical diagram of preparing a garment model using a garment-stitching approach, according to example embodiments of the present disclosure. The garment panels 910 can be obtained, for example, by the garment pre-processor 140 from memory 120. The garment pre-processor 140 can classify the garment panels 910 according to body landmarks. In particular, the garment panels 910 can be classified according to the body landmarks: left arm, right arm, front left side torso, front right side torso, back left side, and back right side. The garment pre-processor 140 can position the garment panels 910 by matching the garment panels with the one or more predetermined body landmarks associated with the body model 901, based on the classification of the garment panels 910. The garment pre-processor 140 can determine a stitching sequence 911, and perform stitching by connecting the garment panels 910 according to the stitching sequence 911 in 3D. The garment pre-processor 140 can perform the stitching of the garment with or without the body model 901 between garment panels.”); and
feeding the supplemental information to the neural network model for predicting the arrangement points (¶ [0035]: “the garment pre-processor can use a probability lookup table, machine-learned feature recognition model, and/or other pattern recognition algorithms to identify the garment feature(s) based on lines or patterns in one or more layers of one or more blocks or pieces in the garment model file.” ¶ [0036]: “In some implementations, the garment pre-processor can position the identified garment panel(s) on a 3D body model. The garment pre-processor can position the identified garment panel(s) based on the body landmark classification of the garment panel(s). The garment pre-processor can receive a 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model. Alternatively, the garment pre-processor can obtain a template 3D body model that includes one or more predetermined body landmark(s) associated with a predetermined region or location on the 3D body model. The garment pre-processor can position and hold the garment panel(s) at a specific region or location on the 3D body model by matching the body landmark classification of the garment panel(s) with the predetermined body landmark(s) associated with the 3D body model, so that the garment panel(s) can be stitched to create a 3D garment model representing the garment.” ¶ [0112] At (603), the method 600 can include positioning the garment panels onto the body model. For example, the garment pre-processor 140 can classify each of the garment panels according to one or more body landmarks, and match the garment panels with the one or more predetermined body landmarks associated with the body model, in order to position the garment panels on the body model.” ¶ [0116]: “FIG. 9 depicts an example graphical diagram of preparing a garment model using a garment-stitching approach, according to example embodiments of the present disclosure. The garment panels 910 can be obtained, for example, by the garment pre-processor 140 from memory 120. The garment pre-processor 140 can classify the garment panels 910 according to body landmarks. In particular, the garment panels 910 can be classified according to the body landmarks: left arm, right arm, front left side torso, front right side torso, back left side, and back right side. The garment pre-processor 140 can position the garment panels 910 by matching the garment panels with the one or more predetermined body landmarks associated with the body model 901, based on the classification of the garment panels 910.”).
Regarding claim 8 (depends on claim 1), FRAKES discloses:
predicting sewing information indicating pairs of line segments of the patterns to be sewn and directions in which the line segments are to be sewn (¶ [0027]: “Stitching can be performed by stitching different edges of a garment cutting pattern according to a specific sequence.” ¶ [0028]: “One or more stitch lines/curves and one or more respective attachment points can be determined for each of the one or more garment panels. A finite element solver can be used to perform stitching by connecting the one or more stitch lines/curves in 3D along the one or more respective attachment points to create a 3D stitched garment.” ¶ [0031]: “In some implementations, a garment pre-processing template can provide a complete set of assembly instructions for an associated garment. The garment pre-processing template can be used while parsing a garment model file. The template can include instructions for positioning the associated garment; identifying stitch lines; and assembling one or more garment panel(s) on a 3D body model. The template can include a set of rules which use a two-dimensional position of the garment panel(s) and the 3D body model to define a transformation required to position the garment panel(s) around the 3D body model in a contract free state. The rules align the garment panel(s) so that the 3D body model has minimal interference with the garment as the garment panel(s) are stitched. The template can also use one or more boundary identifier(s) in the garment model file to define boundary pairs of garment panel(s) to be stitched during the assembly process. In addition, the template can include rules for handling optional design features in the garment model file (e.g., identified through automated feature recognition). The garment pre-processing template can include an orientation, order, and direction of assembly for the garment panel(s) so that the assembly around the 3D body model can be performed in a stable and efficient manner. The garment stitching can occur either sequentially between two garment panels (e.g., similar to manually stitching two pieces of cloth) or in a single step (e.g., fusing two pieces of cloth instantaneously). Multiple pairs of garment panels can also be fused concurrently. ¶ [0040]: “the garment pre-processor can determine an appropriate stitching sequence to stitch garment panel(s) to create a 3D garment model representing a garment. The stitching sequence can be based on, for example, a size, position, material properties, garment feature(s), etc. of the garment panel(s). The stitching sequence can include, for example, a stitching order and/or different stitching techniques for stitching the garment panel(s) along one or more stitch lines/curves and one or more attachment points for the garment panel(s). In some implementations, a rules based approach can be used to determine the stitching sequence. For example, a rule may specify that panels classified as a first type must be stitched prior to panels classified as a second type, and so forth according to rules that describe different priorities of panel types and combinations of panel types.” ¶ [0073]: “In some implementations, the garment pre-processor 140 can prepare the garment model by stitching one or more garment panels along one or more stitch lines or curves. For example, the garment pre-processor 140 can generate a garment model of a garment based at least in part on one or more garment panels associated with the garment. The garment pre-processor 140 can obtain the one or more garment panels from the memory 120. The garment pre-processor 140 can determine one or more stitch lines/curves and one or more respective attachment points for each of the one or more garment panels.” ¶ [0113]: “At (604), the method 600 can include stitching the garment panels to prepare the garment model. For example, the garment pre-processor 140 can analyze the garment panels, and determine a stitching sequence based on a size, position, material properties, or garment features associated with the garment panels. The stitching sequence can include, for example, a stitching order and/or different stitching techniques for stitching the garment panels along one or more stitch lines/curves and one or more attachment points. The garment pre-processor 140 can stitch each of the garment panels along the one or more stitch lines/curves and one or more respective attachment points.”).
Regarding claim 22 (depends on claim 1), FRAKES teaches that the arranging of at least the subset of patterns comprises:
arranging a superimposing pattern of the patterns (e.g., such as a chest pocket panel of a t-shirt; ¶ [0029]: “garment feature” which can include a ¶ [0029]: “pocket”) on a base pattern to which the superimposing pattern is imposed (e.g., such as the front body panel of a t-shirt; ¶ [0029]: “Each of the garment panel(s) can also have or otherwise be associated with one or more garment feature(s).”) (¶ [0029]: “The garment panel(s) can correspond to cutting patterns for the garment. The garment panel(s) can be represented within the garment model file, for example, as blocks or pieces. Each of the garment panel(s) can also have or otherwise be associated with one or more garment feature(s). The garment feature(s) can include, for example, a dart, pocket, placket, j-curve, yolk, dart, embroidery, buttons, etc. The garment feature(s) can be represented within the garment model file, for example, as lines or patterns in one or more layers of each block or piece.” NOTE: In other words, a pocket garment feature can be a pattern in a layer of a garment panel block or piece. ¶ [0035]: “In some implementations, optionally subsequent to classifying the garment panel, the garment pre-processor can identify one or more garment feature(s) associated with a garment panel in a garment model file. For example, the garment pre-processor can use a probability lookup table, machine-learned feature recognition model, and/or other pattern recognition algorithms to identify the garment feature(s) based on lines or patterns in one or more layers of one or more blocks or pieces in the garment model file. The pattern recognition algorithm for identifying the one or more garment feature(s) can be similar to the pattern recognition algorithm to classify each panel in the garment model file described above.” ¶ [0113]: “At (604), the method 600 can include stitching the garment panels to prepare the garment model. For example, the garment pre-processor 140 can analyze the garment panels, and determine a stitching sequence based on a size, position, material properties, or garment features associated with the garment panels. The stitching sequence can include, for example, a stitching order and/or different stitching techniques for stitching the garment panels along one or more stitch lines/curves and one or more attachment points. The garment pre-processor 140 can stitch each of the garment panels along the one or more stitch lines/curves and one or more respective attachment points.” NOTE: If the garment is a common t-shirt including a chest pocket “garment feature” sewn onto the front panel of the t-shirt (which is a well-known common t-shirt construction, an example of which is shown below), in order to correctly assemble the garment, one of ordinary skill in the art would understand that the exterior pocket panel (i.e., the superimposing pattern) would, by necessity, need to be arranged superimposed onto the front body panel of the t-shirt (i.e., the base pattern) in order to be stitched onto the front panel of the t-shirt. In other words, if the garment shown in FIG. 9 included a garment feature comprising a front exterior chest pocket, the panel of the chest pocket would have to be arranged superimposed one of the front torso panels shown in FIG. 9.).
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Regarding claim 23 (depends on claim 1), whereas FRAKES and GOLDBERG may not be explicit as to, JU further teaches that the patterns comprise (e.g., ¶ [0024]: “The clothing patterns may include”):
information on corresponding predefined points (e.g., ¶ [0024]: “information corresponding to the bounding volumes and the arrangement points.”) and the arrangement plates (e.g., ¶ [0024]: “information corresponding to the bounding volumes and the arrangement points.”) comprising the predefined points (e.g., ¶ [0018]: “arrangement points on the bounding volumes” ¶ [0025]: “arrangement points on the bounding volumes”) (¶ [0024]: “The clothing patterns may include information corresponding to the bounding volumes and the arrangement points.” ¶ [0093]: “The display apparatus may grasp the arrangement points 510 on the bounding volumes, on which the clothing patterns 530 need to be placed, based on the information included in the clothing patterns 530 and may determine the initial locations of the clothing patterns 530. claim 19: “wherein the clothing patterns include information corresponding to the bounding volumes and the arrangement points.”). Thus, in order to obtain a garment modeling system having the cumulative features and/or functionalities taught by FRAKES and JU, it would have been obvious to one of ordinary skill in the art to have modified FRAKES so as to incorporate patterns comprising information on corresponding predefined points and arrangement plates comprising the predefined points, as taught by JU.
Regarding claim 24 (depends on claim 1), FRAKES discloses:
wherein the pattern information comprises an image of each of the patterns (e.g., ¶ [0028]: “the one or more garment panels can be stored as a two-dimensional (2D) cutting pattern, a 2D mesh file, a 3D mesh file, or any other suitable format.” ¶ [0029]: “In some implementations, a 3D garment model can be represented by or generated from or based on a computer-aided design (CAD) model file (e.g., .DXF file). The garment model file can include data representing a garment type and one or more garment panel(s). The garment type can correspond to a general classification of a garment, such as, for example, a shirt, pants, top, dress, etc. The garment panel(s) can correspond to cutting patterns for the garment. The garment panel(s) can be represented within the garment model file, for example, as blocks or pieces. Each of the garment panel(s) can also have or otherwise be associated with one or more garment feature(s). The garment feature(s) can include, for example, a dart, pocket, placket, j-curve, yolk, dart, embroidery, buttons, etc. The garment feature(s) can be represented within the garment model file, for example, as lines or patterns in one or more layers of each block or piece.” ¶ [0030]: “one or more garment panel(s) in a garment model file (e.g., CAD file, DXF) using pattern recognition.” ¶ [0032]: “the garment pre-processor can parse a garment model file (e.g., CAD file) to identify one or more garment panel(s), and classify each of the identified garment panel(s).” NOTE: As is well-known in the art, DXF is short for Drawing Exchange Format or Drawing Interchange Format. A DXF file stores images as vector images (i.e., images stored in a vector format). Clearly, one of ordinary skill in the art would understand that the 2D cutting patterns (i.e., “blocks or pieces”) in a DXF file (which can include lines or patterns in each block or piece) can be vector images of each of the panels (i.e., “patterns).).
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over FRAKES et al. (US 2020/0027155) in view of GOLDBERG et al. (US 2011/0245633) and JU (US 2021/0056754), further in view of CEYLAN AKSIT et al. (US 2023/0326137, hereinafter “CEYLAN AKSIT”).
Regarding claim 9 (depends on claim 1), whereas FRAKES, GOLDBERG and JU may not be explicit as to, CEYLAN AKSIT teaches:
wherein the neural network model is trained (e.g., ¶ [0055]: “training subsystem trains the machine learning models”) by backpropagating a difference (e.g., ¶ [0055]: “An error between output generated by decoder neural network and the ground truth is backpropagated.”) between predicted sewing information (e.g., ¶ [0055]: “rendered garment R.sub.t.sup.p synthesized by the network”) and correct sewing information (e.g., ¶ [0055]: “ground truth image” NOTE: In other words, the difference between the rendered garment synthesized by the network and the garment in ground truth image represents a difference between a garment sewn correctly and a garment sewn based on predicted sewing information.) (¶ [0055]: “In some embodiments, the training subsystem trains the machine learning models prior to the processing of FIGS. 3 and 4. In some embodiments, the training subsystem of the rendering system trains the decoder of the second machine learning model G and the neural texture subsystem F end-to-end and jointly. Given a rendered garment R.sub.t.sup.p synthesized by the network and a ground truth image I.sub.t.sup.p, one or more loss functions are minimized. For example, the decoder is a generator of a GAN implemented as a neural network (e.g., a decoder neural network). An error between output generated by decoder neural network and the ground truth is backpropagated. Values of weights associated with connections in the decoder are updated. The process is repeated, computing the loss and readjusting the weights, until an output error is below a predetermined threshold. The training subsystem performs multiple iterations of a training procedure to minimize a loss function to update values of parameters of the decoder (e.g., the weights). In some implementations the loss function includes a perceptual loss and an adversarial loss.”).
Thus, in order to train the machine-learned pattern recognition model (e.g., neural network) used in the garment simulation system taught by the combination FRAKES and JU, it would have been obvious to one of ordinary skill in the art to have trained the neural network by backpropagating a difference between predicted sewing information and correct sewing information, as taught by CEYLAN AKSIT.
Regarding claim 10 (depends on claim 1), whereas FRAKES, GOLDBERG and JU may not be explicit as to, CEYLAN AKSIT teaches:
wherein the neural network model is trained (e.g., ¶ [0055]: “training subsystem trains the machine learning models”) by backpropagating a loss (e.g., ¶ [0055]: “An error between output generated by decoder neural network and the ground truth is backpropagated.” ¶ [0055]: “to minimize a loss function”) derived from a length between line segments of the patterns to be sewn to each other (e.g., ¶ [0055]: “Given a rendered garment R.sub.t.sup.p synthesized by the network and a ground truth image I.sub.t.sup.p, one or more loss functions are minimized.” NOTE: One of ordinary skill in the art would understand that differences in a length between line segments of patterns sewn to each other in the rendered garment and line segments of the patterns sewn together in the ground truth image are one possible error (out of limited number of possible errors) between output generated by decoder neural network and the ground truth to minimized by backpropagation.) (¶ [0055]: “In some embodiments, the training subsystem trains the machine learning models prior to the processing of FIGS. 3 and 4. In some embodiments, the training subsystem of the rendering system trains the decoder of the second machine learning model G and the neural texture subsystem F end-to-end and jointly. Given a rendered garment R.sub.t.sup.p synthesized by the network and a ground truth image I.sub.t.sup.p, one or more loss functions are minimized. For example, the decoder is a generator of a GAN implemented as a neural network (e.g., a decoder neural network). An error between output generated by decoder neural network and the ground truth is backpropagated. Values of weights associated with connections in the decoder are updated. The process is repeated, computing the loss and readjusting the weights, until an output error is below a predetermined threshold. The training subsystem performs multiple iterations of a training procedure to minimize a loss function to update values of parameters of the decoder (e.g., the weights). In some implementations the loss function includes a perceptual loss and an adversarial loss.” ).
Thus, in order to train the machine-learned pattern recognition model (e.g., neural network) used in the garment simulation system taught by the combination of FRAKES and JU, it would have been obvious to one of ordinary skill in the art to have trained the neural network by backpropagating a loss derived from a length between line segments of the patterns to be sewn to each other, as taught by CEYLAN AKSIT.
Allowable Subject Matter
Claims 2-5, 11-16 and 21 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.
Claims 17-20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed June 13, 2026 with respect to independent claims 1, 25 and 26 have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
At present, it is not apparent to the examiner which part of the application could serve as a basis for new and allowable claims. However, should the applicant nevertheless regard some particular matter as patentable, the examiner encourages applicant to appropriately amend the claims to include such matter and to indicate in the REMARKS the difference(s) between the prior art and the claimed invention as well as the significance thereof.
Furthermore, should applicant decide to amend the claims, examiner respectfully requests that the applicant please indicate in the REMARKS from which page(s), line(s) or claim(s) of the originally filed application that any amendments are derived. See MPEP § 2163(II)(A) (There is a strong presumption that an adequate written description of the claimed invention is present in the specification as filed, Wertheim, 541 F.2d at 262, 191 USPQ at 96; however, with respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims.).
Action is Final
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT PEREN who can be reached by telephone at (571) 270-7781, or via email at vincent.peren@uspto.gov. The examiner can normally be reached on Monday-Friday from 10:00 A.M. to 6:00 P.M.
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/VINCENT PEREN/
Examiner, Art Unit 2617
/KING Y POON/Supervisory Patent Examiner, Art Unit 2617
