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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/10/2026 has been entered.
Response to Amendment
Claims 1-21 are pending. Claims 1, 16, and 21 are amended.
Response to Arguments
Applicant's arguments filed 8/10/2026 have been fully considered but they are not persuasive.
Citing various portions of Gronau reference, applicant concludes that,
neither the cited portions of Gronau nor any other portions of Gronau disclose combining any two-dimensional texture and "a two-dimensional neutral template texture map depicting evenly distributed illumination," as recited in claim 1
[See applicant’s response of 8/10/2026]
Examiner does not agree with Applicant’s arguments and conclusions drawn therefrom. E.g., Illumination correction as mentioned in ¶0174, eliminates border lines between different textures boundaries when blended according to texture map, acting as a template. Border line elimination is possible when illumination among all texture maps are evenly illuminated. If darker and brighter textures are blended/mixed/merged to generate the 3D avatar, border lines between differently illuminated textures would be apparent. This teaching is solidified in other portions of the specification, e.g., from ¶0185, ¶0203-0210, ¶0328, ¶0334 …etc.
E.g., in ¶0185 Gronau discloses –
A generative Adversarial Network (GAN) may also be used in order to correct illumination in the texture map of the model, for example in cases where the user's face may be not illuminated uniformly, e.g. there exists a strong illumination from a window at the side of the face or from a spot projector above the user's head.
[Meaning, the goal of Gan is to evenly illuminate facial portions in the texture map]
In ¶0203-0210, Gronau discloses –
The quality of the 3D model and the texture maps may be analysed during or after the process of the creation of the avatar and specifically inspected to verify that all or some of the following cases may be covered:…
f. The illumination may be appropriate and there may be no areas that may be too dark or too bright and saturated
[Meaning, the illumination is evenly distributed across the texture maps used in the 3d avatar generation such that too dark and/or too bright areas are eliminated]
In ¶0328, Gronau discloses setting exposure value in such a fashion that user’s face is not too dark or too bright (meaning, additional conformity is maintained besides usage of GAN and illumination setting, through exposure setting so that darkness and brightness of the texture patches are evenly illuminated).
Similar teaching is found in ¶0334.
Applicant’s specification seeks similar goals as evident from ¶0038, ¶0047, ¶0060 [see PGPUB US 20250157142 A1].
Therefore, based on the arguments provided above, Examiner contends that the teaching of yielding/achieving evenly illuminated texture patches in the blended textures maps in generating the overall 3d avatar is sufficiently disclosed in Gronau.
Aside from what is discussed above, to highlight additional points that Applicant potentially raises –
two-dimensional texture and texture map (based on the amendment made on 8/10/2026) is disclosed sufficiently in Gronau ¶0151, ¶0192, ¶0241… etc.
An argument that “textures are merged according to template texture map” – where “texture” and “texture map” are potentially two different entities, is also disclosed sufficiently in Gronau. Gronau defines ‘texture map’ as –
A texture map is a 2D image in which each color pixel represents the red, green and blue reflectance coefficients of a certain area in the 3D model (¶0151).
The definition includes both texture (which is a 2d image patch comprising pixels) and a template map (the texture is from a certain area of the 3d model). Therefore, “texture map” in Gronau simultaneously indicates both the texture as a 2d image patch and a specific location thereof on the avatar’s face, understood as a template map.
For further details see the rejection below.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9, 11-17, 19-20 are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Gronau et al. (US 20220051412 A1, hereinafter Gronau).
Regarding claim 1, Gronau discloses a computing device (user devices 4000(1)-4000(R), figs. 2-4, ¶0130-¶0139) comprising:
Circuitry (¶0130-¶0139) configured to:
generate a set of two-dimensional textures based at least in part on a set of images that depict a head of a user (¶0106, ¶0436, ¶0460-¶0461, ¶0490-0491, figs. 8-10, 14-16, 20);
generate a two-dimensional blended texture by combining the set of two-dimensional textures and a two-dimensional neutral template texture map depicting evenly distributed illumination (Illumination corrections between the current and previous texture maps can be calculated based on the areas that may be shown in both maps. These corrections may be applied to the current texture map, so that there may be no distinct border line between the textures captured at different times. In addition, in order to avoid sharp transitions between textures from different times, a continuous blending of the textures can be applied, e.g., by using a weighted average of the two texture maps, where the weights change along a transition zone between the textures. The methods mentioned above may be used for merging texture maps, material maps and also 3D models, ¶0174.
A texture map is a 2D image in which each color pixel represents the red, green and blue reflectance coefficients of a certain area in the 3D model. An example of a texture map is shown in FIG. 20. Each color pixel in the texture map corresponds to certain coordinates within a specific polygon (e.g., triangle) on the surface of the 3D model.
An example of a 3D model composed of triangles and the mapping of the texture map to these triangles is shown in FIG. 15.
Generally, each pixel in the texture map has an index of the triangle to which it is mapped and 3 coordinates defining its exact location within the triangle.
A 3D model composed of a fixed number of triangles and vertices may be deformed as the 3D model changes. For example, a 3D model of a face may be deformed as the face changes its expression. Nevertheless, the pixels in the texture map correspond to the same locations in the same triangles, even though the 3D locations of the triangles change as the expression of the face changes.
Texture maps may be constant or may vary as a function of time, expression or of viewing angle. In any case, the correspondence of a given pixel in a texture map and a certain coordinate in a certain triangle in the 3D model doesn't change, ¶0151-0155.
The input to the suggested method may be a 2D monocular video, a templated 3D model of a face (general) with deformation model (per person or general) for this 3D template (specified below) together with an approximation (specific parameters) of the tracked parameters of the first frame of the video: approximated deformation parameters (of the person) in the video and an approximated camera model.
A 3D face template mesh (templated 3D model)—may include a coarse triangular mesh of a generic human face. By coarse, we mean in the order of 5K or 10K polygons, which may be sufficient to represent the general shape but not wrinkles, microstructures or other fine details.
A 3D face deformation model for the template may include a standard parametric way to deform the template and change the general shape of the 3D mesh (jaw structure, nose length, etc), the expression of the face (smile, frown, etc) or the rigid position and orientation of it, based on positions and cues found in the images, ¶0375-0377.
At each frame, the deformed mesh will be referred to as the current 3D face mesh, and its deformation parameters on top of the template may be chosen based on a set of landmarks deduced from the 2D face parts segmentation and the pre-annotated segmentation. To that end, the suggested method may use a 2D face parts segmentation method, in conjunction with a classical 2D rigid registration technique utilizing an ICP (Iterative Closest Point) method to track and deform a model of a 3D face based on an input RGB monocular video.
The suggested method builds upon common face parts segmentation networks, that annotate each pixel with a given face part.
FIG. 13 illustrates face segmentation. Input image 131 may be a color image acquired by a camera. Image 132 illustrates a segmentation of different face parts, visualized by different colors.
In addition, the triangular mesh template may be pre-annotated with a predefined annotation of face parts (e.g. nose, eyes, ears, neck, etc). The mesh annotation may assist in finding correspondences between various face parts on the 3D model to face parts on a given target image. The face parts annotation may be done only once on the 3D template, such that the same annotation can be used for multiple people automatically. The annotation can be specified by listing the triangle belonging to each face part, or by using UV coordinates for the mesh along with a 2D texture map for colouring face parts in different colors as in FIG. 12. – ¶0380-0383.
According to an embodiment a texture map of a face of the person can be generated based on texture maps of different areas of the face, ¶0460
The generating of the texture map of the face from texture maps of different areas of the face may be executed in any manner and may include, for example, smoothing the borders between the different texture maps of the different areas, and the like, ¶0465.
¶0151, ¶0192, ¶0185, ¶0203-0210, ¶0241, ¶0328, ¶0334 …etc.)
generate a three-dimensional avatar that depicts the head of the user with substantially even illumination by applying the blended texture to a head model (During the process of creating the 3D avatar, the 3D model and the 2D texture maps, the quality of the 3D model that may be created may be evaluated by projecting it onto two dimensional images from different angles using a simple linear geometrical projection or a more complex model of a camera that includes optical distortions. The projections of the 3D model to 2D images may be compared to the images grabbed by the camera or cameras., ¶0192.
A generative Adversarial Network (GAN) may also be used in order to correct illumination in the texture map of the model, for example in cases where the user's face may be not illuminated uniformly, e.g. there exists a strong illumination from a window at the side of the face or from a spot projector above the user's head, ¶0185. Also see ¶0192.
¶0151, ¶0192, ¶0185, ¶0203-0210, ¶0241, ¶0328, ¶0334 …etc.); and
an output device configured to facilitate presentation of the three-dimensional avatar of the user (¶0060-0064, ¶0111-0114, ¶0192, 3d avatar 141, fig. 15, ¶0387).
Regarding claim 2, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to select the set of images for unwrapping into the set of two-dimensional textures due at least in part to the set of images depicting the head of the user from different viewing angles (During the process of creating the 3D avatar, the 3D model and the 2D texture maps, the quality of the 3D model that may be created may be evaluated by projecting it onto two dimensional images from different angles using a simple linear geometrical projection or a more complex model of a camera that includes optical distortions, ¶0192).
Regarding claim 3, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to shape the head model based at least in part on at least one of the set of images (The process is repeated where the comparison of the rendered textured 3D model is performed with several camera images from a set of images, such as from a video sequence. Since there may be many images in the image set or video, at each image the 3D model and texture map may be sampled by the camera at different positions, ¶0251).
Regarding claim 4, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to:
render an estimate of the head model based at least in part on one or more input parameters (The 3D model may have separate parameters for shape, pose and expression, ¶0104.
In one embodiment, a 3D model and texture maps are created before the beginning of the meeting and this model is then animated and rendered at run time according to the user's pose and expressions that are estimated from the video images, ¶0150);
compare the estimate of the head model to the at least one of the set of images (The projections of the 3D model to 2D images may be compared to the images grabbed by the camera or cameras, ¶0192. The projections of the 3D model can be compared to the grabbed 2D images in order to verify that the 3D geometrical structure may be accurate and also that the reflection maps may be accurate, ¶0193); and
update the estimate of the head model by modifying the one or more input parameters based at least in part on a result of the comparison (Step 96 may include monitoring each participant by a user device of the participant, during the conference call, updating parameters of 3D model of each participant accordingly and sending updated parameters (sending may be subjected to communication parameters). Step 98 may include receiving by a user device of each participant updated parameters of 3D models related to other participants and updating the display accordingly to reflect the changes to the model, ¶0231-0232. Also see ¶0277).
Regarding claim 5, Gronau discloses the computing device of claim 4, wherein the circuitry is further configured to:
compare the updated estimate of the head model to the at least one of the set of images according to a loss function (Approximated deformation parameters of the person in the video and an approximated camera model can be found by standard 3DMM fitting techniques, for example by using a face landmark detection method to detect known face parts parameters and optimize the camera and pre-annotated landmarks in a least-squared sense. The initialization does not need to be precise but only approximated and can be generated via commonly known techniques, ¶0378.
Step 175 may include using a deformation model (e.g., a 3DMM as explained above) to deform the face mesh and change the camera parameters such that the projection of the first image 3D features matches the 2D locations of the second image 2D locations, as in a typical sparse landmarks and camera fitting, ¶0396); and
refine the updated estimate of the head model by modifying the one or more input parameters based at least in part on an output rendered by the loss function (Approximated deformation parameters of the person in the video and an approximated camera model can be found by standard 3DMM fitting techniques, for example by using a face landmark detection method to detect known face parts parameters and optimize the camera and pre-annotated landmarks in a least-squared sense. The initialization does not need to be precise but only approximated and can be generated via commonly known techniques, ¶0378.
Step 175 may include using a deformation model (e.g., a 3DMM as explained above) to deform the face mesh and change the camera parameters such that the projection of the first image 3D features matches the 2D locations of the second image 2D locations, as in a typical sparse landmarks and camera fitting, ¶0396).
Regarding claim 6, Gronau discloses the computing device of claim 5, wherein the circuitry is further configured to iteratively compare the updated estimate of the head model to the at least one of the set of images and refine the updated estimate of the head model until the output rendered by the loss function satisfies a certain threshold (Approximated deformation parameters of the person in the video and an approximated camera model can be found by standard 3DMM fitting techniques, for example by using a face landmark detection method to detect known face parts parameters and optimize the camera and pre-annotated landmarks in a least-squared sense. The initialization does not need to be precise but only approximated and can be generated via commonly known techniques, ¶0378.
Step 175 may include using a deformation model (e.g., a 3DMM as explained above) to deform the face mesh and change the camera parameters such that the projection of the first image 3D features matches the 2D locations of the second image 2D locations, as in a typical sparse landmarks and camera fitting, ¶0396
At each frame, the deformed mesh will be referred to as the current 3D face mesh, and its deformation parameters on top of the template may be chosen based on a set of landmarks deduced from the 2D face parts segmentation and the pre-annotated segmentation. To that end, the suggested method may use a 2D face parts segmentation method, in conjunction with a classical 2D rigid registration technique utilizing an ICP (Iterative Closest Point) method to track and deform a model of a 3D face based on an input RGB monocular video, ¶380. Finding closest point mandates comparison against an inherent threshold.).
Regarding claim 7, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to:
render the head model (The 3D model and texture map may be used to render an image of the head and/or body or the person, ¶0340);
compare one or more facial features represented in the head model to one or more facial features identified in the at least one of the set of images (Comparing the location of facial landmarks such as the corners of the eyes and lips, the tip and edges of the nose and the edges of the cheeks and chin, that may be found in the image pairs, ¶0195); and
modify the one or more facial features represented in the head model based at least in part on a result of the comparison (Each texture map may be selected and/or augmented based on at least one out of shape, pose and expression. The augmentation may include, modifying values due to lighting, facial make-up effects (lipstick, blush and the like . . . ), adding or removing facial hair features (such as beard, moustache), accessories (such as eyeglasses, ear buds) and the like, ¶0435).
Regarding claim 8, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to unwrap the set of images into the set of two-dimensional textures by flattening a depiction of a face of the user in the set of images to fit across a set of segmentation masks (In addition, the triangular mesh template may be pre-annotated with a predefined annotation of face parts (e.g. nose, eyes, ears, neck, etc). The mesh annotation may assist in finding correspondences between various face parts on the 3D model to face parts on a given target image. The face parts annotation may be done only once on the 3D template, such that the same annotation can be used for multiple people automatically. The annotation can be specified by listing the triangle belonging to each face part, or by using UV coordinates for the mesh along with a 2D texture map for colouring face parts in different colors as in FIG. 12, ¶0383.
An example of a 3D model composed of triangles and the mapping of the texture map to these triangles is shown in fig. 15, ¶0152.
Step 171 may include using the previous iteration's model of the deformed face mesh and the camera screen space projection parameters, the method uses the camera's extrinsic and intrinsic parameters to perform a perspective projection on the 3D face mesh to get the 2D screen space pixel locations of each visible annotated face part vertex. Using the 3D pre-annotation (FIG. 15—see 3D model 141 and UV map 142) the method finds the 2D position of vertices in each face part by matching the annotations, ¶0387).
Regarding claim 9, Gronau discloses the computing device of claim 1, wherein generating he blended texture comprises:
sequentially blending portions of the set of two-dimensional textures (
All the models created from multiple images may be merged into one 3D model or into several different models that vary with the expression or illumination conditions, but all have common shape parameters, ¶0188.
Illumination corrections between the current and previous texture maps can be calculated based on the areas that may be shown in both maps. These corrections may be applied to the current texture map, so that there may be no distinct border line between the textures captured at different times. In addition, in order to avoid sharp transitions between textures from different times, a continuous blending of the textures can be applied, e.g., by using a weighted average of the two texture maps, where the weights change along a transition zone between the textures. The methods mentioned above may be used for merging texture maps, material maps and also 3D models, ¶0174); and
applying the sequentially blended portions of the set of two-dimensional textures to a template texture map (In addition, the triangular mesh template may be pre-annotated with a predefined annotation of face parts (e.g. nose, eyes, ears, neck, etc). The mesh annotation may assist in finding correspondences between various face parts on the 3D model to face parts on a given target image. The face parts annotation may be done only once on the 3D template, such that the same annotation can be used for multiple people automatically. The annotation can be specified by listing the triangle belonging to each face part, or by using UV coordinates for the mesh along with a 2D texture map for colouring face parts in different colors as in FIG. 12, ¶0383.
The new 3D model and texture map may be again rendered to obtain a second rendered image that is compared to the original camera image to create a second difference image that may be used as feedback for enhancing the resolution of the 3D model and texture map. This process may be repeated a given number of times or until a certain criterion is met, e.g., the difference between the actual camera image and the rendered image is below a certain threshold. The process is repeated where the comparison of the rendered textured 3D model is performed with several camera images from a set of images, such as from a video sequence. Since there may be many images in the image set or video, at each image the 3D model and texture map may be sampled by the camera at different positions, ¶0250-0251).
Regarding claim 11, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to mitigate, in the three-dimensional avatar, directional illumination depicted on the head of the user in the set of images (¶0174, ¶0185, ¶0292).
Regarding claim 12, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to evenly illuminate facial features of the user in the three-dimensional avatar despite the facial features being unevenly illuminated in the set of images (¶0174, ¶0185, ¶0292).
Regarding claim 13, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to refine the set of two-dimensional textures via a neural network architecture (¶0151-0155, ¶0183, ¶0174, ¶0254-0256, ¶0325, ¶0355, ¶0451-0453, ¶0491).
Regarding claim 14, Gronau discloses the computing device of claim 13, wherein the neural network architecture comprises at least one of:
a U-Net;
an artificial neural network; or a convolutional neural network (¶0175, 0254-0256, ¶0325, ¶0333, ¶0363).
Regarding claim 15, Gronau discloses the computing device of claim 1, wherein the circuitry comprises a pipeline equipped with a plurality of data processing elements configured to generate the three- dimensional avatar from the set of images (¶0175, ¶0254).
Regarding claim 16, Gronau discloses a system (systems shown in figs. 2-4) comprising:
a camera configured to capture a set of images that depict a head of a user (This may be obtained by modifying the rendered image according to movements of the viewer and the viewer's eyes, thus creating a 3D effect. In order to do this, an image of the viewer is acquired by a camera such as a webcam., ¶0295); and
a computing device (user devices 4000(1)-4000(R), figs. 2-4, ¶0130-¶0139) configured to:
generate a set of two-dimensional textures based at least in part on the set of images (The generating of the 3D model and one or more texture maps may be based on images of the participant that were acquired under different circumstances., ¶0177);
generate a two-dimensional blended texture by combining the set of two-dimensional textures and a two-dimensional neutral template texture map depicting evenly distributed illumination (Illumination corrections between the current and previous texture maps can be calculated based on the areas that may be shown in both maps. These corrections may be applied to the current texture map, so that there may be no distinct border line between the textures captured at different times. In addition, in order to avoid sharp transitions between textures from different times, a continuous blending of the textures can be applied, e.g., by using a weighted average of the two texture maps, where the weights change along a transition zone between the textures. The methods mentioned above may be used for merging texture maps, material maps and also 3D models, ¶0174.
A texture map is a 2D image in which each color pixel represents the red, green and blue reflectance coefficients of a certain area in the 3D model. An example of a texture map is shown in FIG. 20. Each color pixel in the texture map corresponds to certain coordinates within a specific polygon (e.g., triangle) on the surface of the 3D model.
An example of a 3D model composed of triangles and the mapping of the texture map to these triangles is shown in FIG. 15.
Generally, each pixel in the texture map has an index of the triangle to which it is mapped and 3 coordinates defining its exact location within the triangle.
A 3D model composed of a fixed number of triangles and vertices may be deformed as the 3D model changes. For example, a 3D model of a face may be deformed as the face changes its expression. Nevertheless, the pixels in the texture map correspond to the same locations in the same triangles, even though the 3D locations of the triangles change as the expression of the face changes.
Texture maps may be constant or may vary as a function of time, expression or of viewing angle. In any case, the correspondence of a given pixel in a texture map and a certain coordinate in a certain triangle in the 3D model doesn't change, ¶0151-0155.
The input to the suggested method may be a 2D monocular video, a templated 3D model of a face (general) with deformation model (per person or general) for this 3D template (specified below) together with an approximation (specific parameters) of the tracked parameters of the first frame of the video: approximated deformation parameters (of the person) in the video and an approximated camera model.
A 3D face template mesh (templated 3D model)—may include a coarse triangular mesh of a generic human face. By coarse, we mean in the order of 5K or 10K polygons, which may be sufficient to represent the general shape but not wrinkles, microstructures or other fine details.
A 3D face deformation model for the template may include a standard parametric way to deform the template and change the general shape of the 3D mesh (jaw structure, nose length, etc), the expression of the face (smile, frown, etc) or the rigid position and orientation of it, based on positions and cues found in the images, ¶0375-0377.
At each frame, the deformed mesh will be referred to as the current 3D face mesh, and its deformation parameters on top of the template may be chosen based on a set of landmarks deduced from the 2D face parts segmentation and the pre-annotated segmentation. To that end, the suggested method may use a 2D face parts segmentation method, in conjunction with a classical 2D rigid registration technique utilizing an ICP (Iterative Closest Point) method to track and deform a model of a 3D face based on an input RGB monocular video.
The suggested method builds upon common face parts segmentation networks, that annotate each pixel with a given face part.
FIG. 13 illustrates face segmentation. Input image 131 may be a color image acquired by a camera. Image 132 illustrates a segmentation of different face parts, visualized by different colors.
In addition, the triangular mesh template may be pre-annotated with a predefined annotation of face parts (e.g. nose, eyes, ears, neck, etc). The mesh annotation may assist in finding correspondences between various face parts on the 3D model to face parts on a given target image. The face parts annotation may be done only once on the 3D template, such that the same annotation can be used for multiple people automatically. The annotation can be specified by listing the triangle belonging to each face part, or by using UV coordinates for the mesh along with a 2D texture map for colouring face parts in different colors as in FIG. 12. – ¶0380-0383.
According to an embodiment a texture map of a face of the person can be generated based on texture maps of different areas of the face, ¶0460.
The generating of the texture map of the face from texture maps of different areas of the face may be executed in any manner and may include, for example, smoothing the borders between the different texture maps of the different areas, and the like, ¶0465)
generate a three-dimensional avatar that depicts the head of the user with even illumination by applying the blended texture to a head model (During the process of creating the 3D avatar, the 3D model and the 2D texture maps, the quality of the 3D model that may be created may be evaluated by projecting it onto two dimensional images from different angles using a simple linear geometrical projection or a more complex model of a camera that includes optical distortions. The projections of the 3D model to 2D images may be compared to the images grabbed by the camera or cameras., ¶0192.
A generative Adversarial Network (GAN) may also be used in order to correct illumination in the texture map of the model, for example in cases where the user's face may be not illuminated uniformly, e.g. there exists a strong illumination from a window at the side of the face or from a spot projector above the user's head, ¶0185. Also see ¶0192).
Regarding claim 17, Gronau discloses the system of claim 16, wherein the camera comprises a webcam (¶0295).
Regarding claim 19, Gronau discloses the system of claim 16, wherein the computing device is further configured to select the set of images for unwrapping into the set of two-dimensional textures due at least in part to the set of images depicting the head of the user from different viewing angles (During the process of creating the 3D avatar, the 3D model and the 2D texture maps, the quality of the 3D model that may be created may be evaluated by projecting it onto two dimensional images from different angles using a simple linear geometrical projection or a more complex model of a camera that includes optical distortions, ¶0192).
Regarding method claim(s) 20, although wording is different, the material is considered substantively equivalent to the device claim(s) 1 as described above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gronau in view of Lin et al. (US 20210183044 A1, hereinafter Lin).
Regarding claim 10, Gronau discloses the computing device of claim 9, wherein the circuitry is further configured to apply the sequentially blended portions of the set of two-dimensional textures to the template texture map (…a continuous blending of the textures can be applied, ¶0174)
Gronau is not found disclosing the blending is performed using Laplacian pyramids.
However, Lin discloses that blending (fusing) can be done using Laplacian pyramids method (¶0062, 0078, 0097, 0110, 0121, claims 8, 18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to implement the sequentially blended portions of the set of two-dimensional textures to the template texture map of Gronau using one or more Laplacian pyramids as disclosed by Lin, because, combining prior art elements ready to be improved according to known method to yield predictable results is obvious.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gronau in view of Hakura et al. (US 20220157024 A1, hereinafter Hakura).
Regarding claim 21, Gronau discloses the computing device of claim 1, wherein the circuitry is further configured to perform a mask operation to define at least a portion of the set of two-dimensional textures to be blended with the neutral template texture map, wherein the mask operation includes a set of segmentation masks derived from the set of images by applying a face segmentation neural network (¶0182-0183, ¶0375-0377, ¶0380-0383, fig. 7), and wherein generating the two-dimensional blended texture comprises blending the set of two-dimensional textures and the two-dimensional neutral template texture map (¶0174, ¶0182-¶0185, ¶0203-0210, ¶0214, ¶0328, ¶0334, ¶0355 …etc.)
Gronau is not found disclosing expressly that 2D blending of textures and template texture map uses a plurality of Laplacian pyramids.
However, Hakura discloses that parameterized textures are encoded as a multidimensional Laplacian pyramid on fixed size blocks of parameter space (Abstract, Col. 1, lines 66-67, Col. 18, section “Adaptive Laplacian Pyramid”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Gronau such that the parameterized texture map of Gronau (e.g, as disclosed in ¶0183-0184) are encoded as a multidimensional Laplacian pyramid on fixed size blocks of parameter space within or in addition to GAN according to the teaching of Hakura, to obtain, blending of textures and template texture map uses a plurality of Laplacian pyramids, because, combining prior art elements ready to be improved according to known method to yield predictable results is obvious (see MPEP §2143.I). Furthermore, in pyramidal regularization, entire texture can be defined simultaneously allowing arbitrary movement away from the original viewpoint samples (Col, 15, lines 62-65).
Conclusion
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/NURUN FLORA/Primary Examiner, Art Unit 2619
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