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 .
Response to Applicant’s Arguments
Applicant’s arguments filed 02/23/2026 have been fully considered, but they are not deemed to be persuasive based on the two new references of DONG et al (Geometry-aware Two-scale PIFu Representation for Human Reconstruction) and LE et al (Robust and Accurate Skeletal Rigging from Mesh Sequences). Specifically, Dong teaches the new amended feature “obtaining sensor data from one or more sensors of a device, wherein the sensor data corresponds to an image of a physical subject and obtaining front depth data for a front portion of the physical subject” (Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data) (Noted: Dong’s back of the body from a 3D reconstructed object (e.g., figures 6f, 8h) is derived from the input of front depth and the image); and Le teaches other new amended feature “driving an avatar of the physical subject based on additional sensor data and the set of joint locations” (Le, Figure 3: The pipeline of our skeletal rigging approach; Figure 9: The extracted horse-gallop skeletons with different cluster initializations; Figure 15: different poses; 1 Introduction - Skeleton-based mesh deformation is a widely-used method for animating articulated creatures such as humans and animals... Rigging a model currently consists of two main steps: building a hierarchical skeleton with rigid bones connected by joints, and skinning the 3D model to define how joint rotations and translations would propagate to the surface during animation) (Noted: with additional sensor data, the physical subject (e.g., the horse with different classified regions, the human body with different poses) is represented with the driving avatar during animation). Accordingly, the claimed invention as represented in the claims does not represent a patentable distinction over the art of record.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over KUSHWAHA et al (Enhancement of human 3D pose estimation using a novel concept of depth prediction with pose alignment from a single 2D image) in view of DONG et al (Geometry-aware Two-scale PIFu Representation for Human Reconstruction), SMITH et al (11,200,689), HU et al (US- 20220245912) and LE et al (Robust and Accurate Skeletal Rigging from Mesh Sequences).
As per claim 1, Kushwaha teaches the claimed "method" comprising: " obtaining sensor data from one or more sensors of a device " (Kushwaha, Figure 2 - Input Image); "obtaining front depth data for a front portion of the physical subject" (Kushwaha, Figure 3, 3.1. Depth predictor - This module aims to predict depth (z-axis It is noted that value) using anthropometric measurements and lifts the 2D coordinates of body joints into 3D coordinates. It is noted that “the sensor data corresponds to an image of a physical subject and obtaining front depth data for a front portion of the physical subject” is well- known in the art as a laser ranger, a depth camera RGB-D (Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; see also Smith, column 13, lines 57-67 - To improve the depth data, the system 10 may adopt a mesh alignment process to infer the non-visible (e.g., unobserved surface(s) 216) parts of the body geometry based on a statistical model of human shape and pose (e.g., statistical body model). For example, the system 100 may process the back depth data 532 and the front depth data 534 to generate an output mesh and/or avatar. Thus, the system 100 may capture a fixed scan (e.g., back depth data 532 and front depth data 534) and then effectively transfer knowledge of the depth information from the scan over to an output mesh and/or avatar using the statistical body model; column 8, lines 49-57 - given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low-dimensional parametric body model that captures the variability in shape across people and poses; Hu, [0223] - In an example, the model construction parameters may include a depth map of a first surface of the basic model, a semantic map of the first surface of the basic model, a depth map of a second surface of the basic model, and a semantic map of the second surface of the basic model. The first surface and the second surface are surfaces of the basic model. For example, the first surface may be a front side of the basic model, and the second surface may be a back side of the basic model; [0226] - It should be understood that, when the basic model includes a plurality of surfaces, a depth map and a semantic map of each surface may be obtained. For ease of description, the front side is used as the first surface and the back side is used as the second surface below, [0025] - The depth map of the first surface indicates depth information (namely, information about a distance between each vertex on the first surface and the camera) corresponding to each vertex on the first surface of the basic model, and the depth map of the second surface indicates depth information (namely, information about a distance between each vertex on the second surface and the camera) corresponding to each vertex on the second surface of the basic model). It is noted that Kushwaha does not explicitly teach "back depth data" as claimed; however, Kushwaha's realistic 3D avatar generation from 3D poses (e.g., Abstract - Human skeletal 3D posture is the foundation for 3D avatar creation, the construction of human 3D meshes, recognition of human actions or activities, augmented reality, etc) suggests the reconstructed 3D human body which includes a "back depth" information in the claimed step "generating back depth data for a back portion of the physical subject based on the image of the physical subject and the front depth data" (see also Dong, Figures 6f, 8h - Dong’s back of the body from a 3D reconstructed object (e.g., figures 6f, 8h) is derived from the input of front depth and the image; Smith, column 5, lines 7-37 - The first model may be trained to identify a portion of the input image data that is associated with the user 5 and generate two depth estimate values (e.g., front depth estimate and back depth estimate) for each pixel included in the portion of the input image data. For example, the first model may implicitly learn to exclude a background of the input image data, identifying the user 5 in a foreground of the input image data and generating mask data indicating a plurality of pixels associated with the user 5. Based on the pixel values of the plurality of pixels, the first model may generate front depth estimate values (e.g., front depth data) that correspond to an estimated distance between the camera 112 and a front surface of the user 5 for each of the plurality of pixels. Similarly, the first model may generate back depth estimate values (e.g., back depth data) that correspond to an estimated distance between the camera 112 and a back surface of the user 5 for each of the plurality of pixels. Thus, the first model hypothesizes the back side of the user 5 based on the input image data; column 8, lines 49-57 - A database of body scan information may be obtained or generated. For example, the system 100 may access one or more databases that are commercially available. In some examples, given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low-dimensional parametric body model that captures the variability in shape across people and poses); "determining a set of joint locations for the physical subject from the image of the physical subject, the front depth data, and the back depth data; and driving an avatar of the physical subject based on additional sensor data and the set of joint locations" (Kushwasa, Figure 6 - Experimental outcomes of the proposed depth prediction and pose alignment method for human 3D pose estimation on the Human3.6M dataset. We compare our results with the ground-truth annotations given in the dataset. In the figure, the first column shows the images of the dataset that fed into the proposed architecture as an input. The first column and second column in each section of views indicate the output of the proposed method and ground-truth, respectively) (see also Le, Figure 3: The pipeline of our skeletal rigging approach; Figure 9: The extracted horse-gallop skeletons with different cluster initializations; Figure 15: different poses; 1 Introduction - Skeleton-based mesh deformation is a widely-used method for animating articulated creatures such as humans and animals... Rigging a model currently consists of two main steps: building a hierarchical skeleton with rigid bones connected by joints, and skinning the 3D model to define how joint rotations and translations would propagate to the surface during animation) (Noted: with additional sensor data, the physical subject (e.g., the horse with different classified regions, the human body with different poses) is represented with the driving avatar during animation). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by generating the back depth data and the skeleton joints from the captured 2D image information. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 2 adds into claim 1 "wherein determining the set of joint locations comprises: generating, by a trained network, a feature set corresponding to the physical subject based on the image of the physical subject, the front depth data, and the back depth data" (Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; Figures 6f, 8h - Dong’s back of the body from a 3D reconstructed object (e.g., figures 6f, 8h) is derived from the input of front depth and the image; Hu, [0223] - In an example, the model construction parameters may include a depth map of a first surface of the basic model, a semantic map of the first surface of the basic model, a depth map of a second surface of the basic model, and a semantic map of the second surface of the basic model. The first surface and the second surface are surfaces of the basic model. For example, the first surface may be a front side of the basic model, and the second surface may be a back side of the basic model; [0226] - For ease of description, the front side is used as the first surface and the back side is used as the second surface below, [0025] - The semantic map of the first surface indicates semantic information (namely, information about a position of each vertex on the first surface on a body) corresponding to each vertex on the first surface of the basic model, and the semantic map of the second surface indicates semantic information (namely, information about a position of each vertex on the second surface on the body) corresponding to each vertex on the second surface of the basic model). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by generating the back depth data and the skeleton joints from the captured 2D image information. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 3 adds into claim 2 "wherein the feature set corresponds to sample points for the physical subject, the method further comprising: obtaining, for each of the sample points, a classifier value, wherein the classifier value indicates a relationship of a corresponding sample point to a volume corresponding to the physical subject" (Le, Figure 9 - The extracted horse-gallop skeletons with different cluster classifications; Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; Figures 6f, 8h - Dong’s back of the body from a 3D reconstructed object (e.g., figures 6f, 8h) is derived from the input of front depth and the image; Hu, [0039] - For example, semantic information corresponding to the head is 1, namely, semantic information corresponding to each vertex in the head is 1, and semantic information corresponding to the trunk is 2, namely, semantic information corresponding to each vertex in the trunk is 2. In this application, a range of semantic information corresponding to vertices in different body parts in the basic model is different, and semantic information corresponding to any two vertices in a same body part is different, so that semantic information corresponding to any two vertices in the basic model is different; Smith, column 5, lines 19-51 - The first model may be trained to identify a portion of the input image data that is associated with the user 5 and generate two depth estimate values (e.g., front depth estimate and back depth estimate) for each pixel included in the portion of the input image data. For example, the first model may implicitly learn to exclude a background of the input image data, identifying the user 5 in a foreground of the input image data and generating mask data indicating a plurality of pixels associated with the user 5. Based on the pixel values of the plurality of pixels, the first model may generate front depth estimate values (e.g., front depth data) that correspond to an estimated distance between the camera 112 and a front surface of the user 5 for each of the plurality of pixels. Similarly, the first model may generate back depth estimate values (e.g., back depth data) that correspond to an estimated distance between the camera 112 and a back surface of the user 5 for each of the plurality of pixels. Thus, the first model hypothesizes the back side of the user 5 based on the input image data; column 8, lines 49-57 - A database of body scan information may be obtained or generated. For example, the system 100 may access one or more databases that are commercially available. In some examples, given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low- dimensional parametric body model that captures the variability in shape across people and poses; column 8, line 58 to column 9, line 19 - SVM is a supervised learning model with associated learning algorithms that analyze data and recognize patterns in the data, and which are commonly used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. More complex SVM models may be built with the training set identifying more than two categories, with the SVM determining which category is most similar to input data. An SVM model may be mapped so that the examples of the separate categories are divided by clear gaps. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gaps they fall on. Classifiers may issue a "score" indicating which category the data most closely matches. The score may provide an indication of how closely the data matches the category). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by classifying the reconstructed 3D object based on the samples of the sensor captured data. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 4 adds into claim 3 "wherein the back depth data is obtained based on the classifier value for the sample points" (Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; Figures 6f, 8h - Dong’s back of the body from a 3D reconstructed object (e.g., figures 6f, 8h) is derived from the input of front depth and the image; Smith, column 5, lines 19-51 - The first model may be trained to identify a portion of the input image data that is associated with the user 5 and generate two depth estimate values (e.g., front depth estimate and back depth estimate) for each pixel included in the portion of the input image data. For example, the first model may implicitly learn to exclude a background of the input image data, identifying the user 5 in a foreground of the input image data and generating mask data indicating a plurality of pixels associated with the user 5. Based on the pixel values of the plurality of pixels, the first model may generate front depth estimate values (e.g., front depth data) that correspond to an estimated distance between the camera 112 and a front surface of the user 5 for each of the plurality of pixels. Similarly, the first model may generate back depth estimate values (e.g., back depth data) that correspond to an estimated distance between the camera 112 and a back surface of the user 5 for each of the plurality of pixels. Thus, the first model hypothesizes the back side of the user 5 based on the input image data; column 8, lines 49-57 - A database of body scan information may be obtained or generated. For example, the system 100 may access one or more databases that are commercially available. In some examples, given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low- dimensional parametric body model that captures the variability in shape across people and poses; column 8, line 58 to column 9, line 19 - SVM is a supervised learning model with associated learning algorithms that analyze data and recognize patterns in the data, and which are commonly used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. More complex SVM models may be built with the training set identifying more than two categories, with the SVM determining which category is most similar to input data. An SVM model may be mapped so that the examples of the separate categories are divided by clear gaps. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gaps they fall on. Classifiers may issue a "score" indicating which category the data most closely matches. The score may provide an indication of how closely the data matches the category; Hu, [0223] - In an example, the model construction parameters may include a depth map of a first surface of the basic model, a semantic map of the first surface of the basic model, a depth map of a second surface of the basic model, and a semantic map of the second surface of the basic model. The first surface and the second surface are surfaces of the basic model. For example, the first surface may be a front side of the basic model, and the second surface may be a back side of the basic model; [0226] - For ease of description, the front side is used as the first surface and the back side is used as the second surface below, [0025] - The semantic map of the first surface indicates semantic information (namely, information about a position of each vertex on the first surface on a body) corresponding to each vertex on the first surface of the basic model, and the semantic map of the second surface indicates semantic information (namely, information about a position of each vertex on the second surface on the body) corresponding to each vertex on the second surface of the basic model; Le, Figure 9 - The extracted horse-gallop skeletons with different cluster classifications). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by providing a classifier to indicate a relationship of the sample point (e.g., front, back sides of the human body) to a volume corresponding to the physical subject. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 5 adds into claim 1 "wherein the back depth data is obtained from a second network configured to predict the back depth data based on the image of the physical subject and the front depth data" (Hu, [0223] - In an example, the model construction parameters may include a depth map of a first surface of the basic model, a semantic map of the first surface of the basic model, a depth map of a second surface of the basic model, and a semantic map of the second surface of the basic model. The first surface and the second surface are surfaces of the basic model. For example, the first surface may be a front side of the basic model, and the second surface may be a back side of the basic model; [0226] - It should be understood that, when the basic model includes a plurality of surfaces, a depth map and a semantic map of each surface may be obtained. For ease of description, the front side is used as the first surface and the back side is used as the second surface below, [0025] - The depth map of the first surface indicates depth information (namely, information about a distance between each vertex on the first surface and the camera) corresponding to each vertex on the first surface of the basic model, and the depth map of the second surface indicates depth information (namely, information about a distance between each vertex on the second surface and the camera) corresponding to each vertex on the second surface of the basic model. The semantic map of the first surface indicates semantic information (namely, information about a position of each vertex on the first surface on a body) corresponding to each vertex on the first surface of the basic model, and the semantic map of the second surface indicates semantic information (namely, information about a position of each vertex on the second surface on the body) corresponding to each vertex on the second surface of the basic model; Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; see also Smith, column 13, lines 57-67 - To improve the depth data, the system 10 may adopt a mesh alignment process to infer the non-visible (e.g., unobserved surface(s) 216) parts of the body geometry based on a statistical model of human shape and pose (e.g., statistical body model). For example, the system 100 may process the back depth data 532 and the front depth data 534 to generate an output mesh and/or avatar. Thus, the system 100 may capture a fixed scan (e.g., back depth data 532 and front depth data 534) and then effectively transfer knowledge of the depth information from the scan over to an output mesh and/or avatar using the statistical body model; column 8, lines 49-57 - given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low-dimensional parametric body model that captures the variability in shape across people and poses). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by generating the back depth data and the front depth data from the captured 2D image information. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 6 adds into claim 1 "wherein the front depth data is obtained by applying the image of the physical subject and depth sensor data to a second network configured to predict the front depth data, wherein the depth sensor data is captured in accordance with the image of the physical subject" (Hu, [0223] - In an example, the model construction parameters may include a depth map of a first surface of the basic model, a semantic map of the first surface of the basic model, a depth map of a second surface of the basic model, and a semantic map of the second surface of the basic model. The first surface and the second surface are surfaces of the basic model. For example, the first surface may be a front side of the basic model, and the second surface may be a back side of the basic model; [0226] - It should be understood that, when the basic model includes a plurality of surfaces, a depth map and a semantic map of each surface may be obtained. For ease of description, the front side is used as the first surface and the back side is used as the second surface below, [0025] - The depth map of the first surface indicates depth information (namely, information about a distance between each vertex on the first surface and the camera) corresponding to each vertex on the first surface of the basic model, and the depth map of the second surface indicates depth information (namely, information about a distance between each vertex on the second surface and the camera) corresponding to each vertex on the second surface of the basic model. The semantic map of the first surface indicates semantic information (namely, information about a position of each vertex on the first surface on a body) corresponding to each vertex on the first surface of the basic model, and the semantic map of the second surface indicates semantic information (namely, information about a position of each vertex on the second surface on the body) corresponding to each vertex on the second surface of the basic model; Dong, Figure 2: Proposed method overview – the input of RGBD image of a depth image (512x512) and an image (512x512) of a physical subject; Figure 8 – shows the reconstruction results of five existing methods and our method on our captured real data; see also Smith, column 13, lines 57-67 - To improve the depth data, the system 10 may adopt a mesh alignment process to infer the non-visible (e.g., unobserved surface(s) 216) parts of the body geometry based on a statistical model of human shape and pose (e.g., statistical body model). For example, the system 100 may process the back depth data 532 and the front depth data 534 to generate an output mesh and/or avatar. Thus, the system 100 may capture a fixed scan (e.g., back depth data 532 and front depth data 534) and then effectively transfer knowledge of the depth information from the scan over to an output mesh and/or avatar using the statistical body model; column 8, lines 49-57 - given a database of 3D laser ranges scans of human bodies, the system 100 may align the bodies and then apply statistical learning methods within a statistical learning system (e.g., trained model) to learn a low-dimensional parametric body model that captures the variability in shape across people and poses). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by generating the back depth data and the front depth data from the captured 2D image information. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claim 7 adds into claim 1 "determining a skeleton for the physical subject based on the set of joint locations and inverse kinematics solver" (Nakamura, Abstract - Each join angle is obtained by performing an optimization calculation based on inverse kinematics using the candidates and the articulated structure. Positions of the feature points including the joints are obtained by performing a forward kinematics calculation using the joint angles Le, Figure 3: The pipeline of our skeletal rigging approach; Figure 9: The extracted horse-gallop skeletons with different cluster initializations; Figure 15: different poses; 1 Introduction - Skeleton-based mesh deformation is a widely-used method for animating articulated creatures such as humans and animals... Rigging a model currently consists of two main steps: building a hierarchical skeleton with rigid bones connected by joints, and skinning the 3D model to define how joint rotations and translations would propagate to the surface during animation) (Noted: with additional sensor data, the physical subject (e.g., the horse with different classified regions, the human body with different poses) is represented with the driving avatar during animation). Since the references are related in the generation of 3D object from the inputted 2D image of the object, it would have been obvious, in view of Dong, Smith, Hu and Le, to configure Kushwasa's method as claimed by generating the back depth data and the skeleton joints from the captured 2D image information using inverse kinematics solver. The motivation is to construct a 3D human body in form of a mesh model (Kushwasa, Abstract).
Claims 8-14 and 15-20 claim a system and a non-transitory computer readable medium based on the method of claims 1-7; therefore, they are rejected under a similar rationale.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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