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 .
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, 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(s) 1-3, 5-8, 10-12, 14-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (U.S. PGPUB 20180096537) in view of Chang et al. (U.S. PGPUB 20230326136).
With respect to claim 1, Kornilov et al. disclose a computer-implemented method, comprising: capturing, via an application executing on a computing device operated by a user, image data including an initial image of a face of the user (paragraph 32, client device 104 includes or is connected to a camera device. The camera device and/or a processor of client device 104 that is running an application can capture a set of images of the user's head as user 102 turns his or her head in different directions, paragraph 48, At 302, a 3D model of a user's face is obtained, wherein the 3D model of the user's face comprises a plurality of 3D points. In some embodiments, the 3D model of the user's face is determined from a set of images of a user's face);
identifying an indexing node in the user mesh corresponding to a set portion of the face of the user captured in the image data (paragraph 40, the 3D model of the user's face comprises a set of points in 3D space that define a set of reference points associated with (e.g., the locations of) features on the user's face from the associated set of images, paragraph 48, the 3D model of the user's face comprises reference points in 3D space that define the locations of various facial features of the user. For example, the 3D points of the 3D model of the user's face comprise the two internal eyebrow points, the two internal eye corners, the two external eye corners, the two ear junctures, the nose bridge, the cheekbones, and the nose tip);
identifying an indexing node in a reference mesh, the indexing node of the reference mesh corresponding to a set portion of the reference mesh (paragraph 42, the predetermined 3D face comprises a 3D model of a generic face. In some embodiments, the predetermined 3D face includes a predetermined set of points along the nose curve, paragraph 82, a known set of 3D points defines the shape of a particular facial feature on the predetermined 3D face. In various embodiments, the particular facial feature is the nose and the set of 3D points defines the nose curvature), the set portion of the reference mesh corresponding to the set portion of the user mesh (paragraph 84, morphing the predetermined 3D face to correspond to the 3D model of a user's face comprises moving the locations of at least some facial features on the predetermined 3D face to match the corresponding 3D points associated with various facial features of the 3D model of the user's face);
positioning a virtual frame of a head mounted wearable device on the reference mesh, at a position corresponding to the indexing node of the reference mesh (paragraph 91, The segment that is the closest to the initial placement of the 3D model of the glasses frame (e.g., the initial placement of the 3D model of the glasses frame is determined using a process such as process 1000 of FIG. 10) is determined… determine a particular segment between two adjacent 3D points along the morphed nose curvature that is the shortest distance (and is therefore the closest) to the segment between the bridge points of the 3D model of the glasses frame to determine the initial placement of the 3D model of the glasses frame);
projecting the reference mesh and the virtual frame onto the user mesh (paragraph 95, the 3D points along the facial feature of the nose curvature from a morphed 3D face (e.g., a predetermined 3D face that has been morphed to correspond to a 3D model of a user's face)); and
adjusting a position of the virtual frame to correspond to the indexing node of the user mesh (paragraph 95, 3D model of the glasses frame 1702 is then moved towards the segment along normal 1708 until the bridge points (including bridge point 1714) of 3D model of the glasses frame 1702 are close to the determined segment). Although Kornilov et al. disclose use of a mesh model (paragraph 82, the predetermined 3D face is a mesh model); Kornilov et al. do not expressly disclose generating a user mesh, the user mesh being representative of a face of the user based on the image data.
Chang et al., who also deal with 3D models, disclose a method for generating a user mesh, the user mesh being representative of a face of the user based on the image data (paragraph 54, the one or more of CPU 116 and GPU 118 may also perform operations that generate mesh data characterizing a mesh of the captured image based on the coefficient data, paragraph 86, Further, and at step 406, the image capture device 100 generates, based on the coefficient data, first mesh data characterizing a mesh of the image).
Kornilov et al. and Chang et al. are in the same field of endeavor, namely computer graphics.
Before the effective filing date of the claimed invention, it would have been obvious to apply the method of generating a user mesh, the user mesh being representative of a face of the user based on the image data, as taught by Chang et al., to the Kornilov et al. system, because this would represent the face model using a traditional, well-known data structure in computer graphics.
With respect to claim 2, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1, wherein: identifying the indexing node in the user mesh includes identifying a sellion node in the user mesh (Kornilov et al.: paragraph 48, the 3D points of the 3D model of the user's face comprise the two internal eyebrow points, the two internal eye corners, the two external eye corners, the two ear junctures, the nose bridge), the sellion node corresponding to a position of a sellion portion of the face of the user captured in the image data (Kornilov et al.: paragraph 48, the 3D model of the user's face is determined from a set of images of a user's face. The set of images includes different angles and/or orientations of the user's face. In some embodiments, the 3D model of the user's face comprises reference points in 3D space that define the locations of various facial features of the user); and
identifying the indexing node in the reference mesh includes identifying a sellion node in the reference mesh, the sellion node corresponding to a position of a sellion portion of a face represented by the reference mesh (Kornilov et al.: paragraph 95, the 3D points along the facial feature of the nose curvature from a morphed 3D face (e.g., a predetermined 3D face that has been morphed to correspond to a 3D model of a user's face) are each denoted by an angled “X.” A segment is defined (not shown) between every pair of adjacent 3D points along the nose curvature and the segment between 3D point 1704 and 3D point 1706 is determined to be the closest segment to 3D model of a glasses frame 1702). Point 1704 corresponds to the sellion.
With respect to claim 3, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1, wherein projecting the reference mesh and the virtual frame onto the user mesh includes performing a rigid transformation of the reference mesh and the virtual frame to the user mesh (Kornilov et al.: paragraph 89, As a result of morphing the predetermined 3D face, the previously selected 3D points along the nose curvature have been moved to new locations in 3D space (i.e., new 3D (x, y, z) coordinates)).
With respect to claim 5, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1, wherein generating the user mesh includes: detecting one or more facial landmarks in the image data (Chang et al.: paragraph 85, At step 404, the image capture device 100 applies a first trained machine learning process to the image data to generate coefficient data characterizing the image in a plurality of dimensions. For example, image capture device 100 may obtain, from instruction memory 132, 3D reconstruction 132A characterizing configuration parameters, hyperparameters, and/or weights for a trained 3D fitting model, and may configure a 3D fitting model based on the obtained configuration parameters, hyperparameters, and/or weights. Further, image capture device 100 applies the configured 3D fitting model to the image data causing the configured 3D fitting model to ingest elements of the image data); and generating, by a machine learning model, the user mesh based on the one or more facial landmarks (Chang et al.: paragraph 86, at step 406, the image capture device 100 generates, based on the coefficient data, first mesh data characterizing a mesh of the image). It would have been obvious to apply the method wherein generating the user mesh includes: detecting one or more facial landmarks in the image data; and generating, by a machine learning model, the user mesh based on the one or more facial landmarks, because this would provide more accurate image reconstruction capabilities, such as the reconstruction of 3D images that include objects with variation, such as captured facial images (e.g., 3D face reconstruction). For instance, the embodiments may more accurately reconstruct 3D facial images even though there is large structural variation across persons' faces (paragraph 23 of Chang et al.).
With respect to claim 6, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1, further comprising: outputting a fitting image, the fitting image including a rendering of the virtual frame, superimposed on the initial image of the face of the user, generated based on the image data, at the position corresponding to the indexing node of the user mesh (Kornilov et al.: paragraph 98, At 1802, the glasses frame is rendered using at least a determined placement of a 3D model of the glasses frame relative to a 3D model of a user's face, a set of extrinsic information corresponding to the image of the user's face, and a 3D face, Kornilov et al.: paragraph 101, At 1804, the 2D image of the glasses frame is overlaid on the image of the user's face).
With respect to claim 7, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 6, wherein adjusting the position of the virtual frame includes: comparing a position of the indexing node of the reference mesh to a position of the indexing node of the user mesh (Kornilov et al.: paragraph 84, morphing the predetermined 3D face to correspond to the 3D model of a user's face comprises moving the locations of at least some facial features on the predetermined 3D face to match the corresponding 3D points associated with various facial features of the 3D model of the user's face, matching the corresponding nodes infers performing comparison), including:
detecting a distance between the indexing node in the reference mesh and the indexing node in the user mesh (Kornilov et al.: paragraph 89, As a result of morphing the predetermined 3D face, the previously selected 3D points along the nose curvature have been moved to new locations in 3D space (i.e., new 3D (x, y, z) coordinates), points are moved at a distance to new locations);
determining a corresponding pixel distance between the indexing node of the reference mesh and the indexing node of the user mesh; and adjusting a position of the virtual frame in the fitting image based on the pixel distance (Kornilov et al.: paragraph 46, Rendering engine 212 is configured to render a 2D image of a glasses frame to be overlaid on an image). By rendering the glasses frame on the user’s face as an image, this infers determining pixel distances, or pixel values for the virtual frame.
With respect to claim 8, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1, wherein capturing the image data includes capturing a two-dimensional image of the face of the user (Kornilov et al.: paragraph 54, From the 2D video frames of the user's face, a 3D model of the user's face can be determined); and wherein the user mesh is a three-dimensional mesh corresponding to the face of the user (Chang et al.: paragraph 54, CPU 116 may apply the 3D fitting model to the captured image and generate coefficient data characterizing the captured image in a plurality of dimensions (e.g., three dimensions)), and the reference mesh is a three-dimensional mesh generated based on previously collected data representing a plurality of subjects (Kornilov et al.: paragraph 82, the predetermined 3D face may be determined from a plurality of historical user faces).
With respect to claim 10, Kornilov et al. as modified by Chang et al. disclose a non-transitory computer-readable medium storing instructions (Kornilov et al.: paragraph 28, The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium) that, when executed by at least one processor of a computing device (Kornilov et al.: paragraph 28, As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions), are configured to cause the at least one processor to execute the method of claim 1; see rationale for rejection of claim 1.
With respect to claim 11, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor to execute the method of claim 2; see rationale for rejection of claim 2.
With respect to claim 12, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor to execute the method of claim 3; see rationale for rejection of claim 3.
With respect to claim 14, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor to execute the method of claim 5; see rationale for rejection of claim 5.
With respect to claim 15, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor to execute the method of claim 6; see rationale for rejection of claim 6.
With respect to claim 16, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 15, wherein the instructions cause the at least one processor to execute the method of claim 7; see rationale for rejection of claim 7.
With respect to claim 17, Kornilov et al. as modified by Chang et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor execute the method of claim 8; see rationale for rejection of claim 8.
With respect to claim 19, Kornilov et al. disclose a system (paragraph 31, FIG. 1 is a diagram showing an embodiment of a system), comprising: a computing device (paragraph 32, Client device 104), including: an image sensor (paragraph 32, client device 104 includes or is connected to a camera device); at least one processor (paragraph 32, The camera device and/or a processor of client device 104 that is running an application can capture a set of images of the user's head); and a memory storing instructions that, when executed by the at least one processor (paragraph 28, The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium…As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions), cause the at least one processor to:
capture image data including an initial image of a face of a user (paragraph 32, The camera device and/or a processor of client device 104 that is running an application can capture a set of images of the user's head as user 102 turns his or her head in different directions, paragraph 48, At 302, a 3D model of a user's face is obtained, wherein the 3D model of the user's face comprises a plurality of 3D points. In some embodiments, the 3D model of the user's face is determined from a set of images of a user's face);
identify a sellion node in the user mesh corresponding to a sellion portion of the face of the user captured in the image data (paragraph 48, the 3D points of the 3D model of the user's face comprise the two internal eyebrow points, the two internal eye corners, the two external eye corners, the two ear junctures, the nose bridge);
identify a sellion node in a reference mesh, the sellion node of the reference mesh corresponding to a sellion portion of the reference mesh (paragraph 95, the 3D points along the facial feature of the nose curvature from a morphed 3D face (e.g., a predetermined 3D face that has been morphed to correspond to a 3D model of a user's face) are each denoted by an angled “X.” A segment is defined (not shown) between every pair of adjacent 3D points along the nose curvature and the segment between 3D point 1704 and 3D point 1706 is determined to be the closest segment to 3D model of a glasses frame 1702; Point 1704 corresponds to the sellion), the sellion portion of the reference mesh corresponding to the sellion portion of the user mesh (paragraph 84, morphing the predetermined 3D face to correspond to the 3D model of a user's face comprises moving the locations of at least some facial features on the predetermined 3D face to match the corresponding 3D points associated with various facial features of the 3D model of the user's face);
position a virtual frame of a head mounted wearable device on the reference mesh, at a position corresponding to the sellion node of the reference mesh (paragraph 91, The segment that is the closest to the initial placement of the 3D model of the glasses frame (e.g., the initial placement of the 3D model of the glasses frame is determined using a process such as process 1000 of FIG. 10) is determined… determine a particular segment between two adjacent 3D points along the morphed nose curvature that is the shortest distance (and is therefore the closest) to the segment between the bridge points of the 3D model of the glasses frame to determine the initial placement of the 3D model of the glasses frame);
project the reference mesh and the virtual frame onto the user mesh (paragraph 95, the 3D points along the facial feature of the nose curvature from a morphed 3D face (e.g., a predetermined 3D face that has been morphed to correspond to a 3D model of a user's face)); and
adjust a position of the virtual frame to correspond to the sellion node of the user mesh (paragraph 95, 3D model of the glasses frame 1702 is then moved towards the segment along normal 1708 until the bridge points (including bridge point 1714) of 3D model of the glasses frame 1702 are close to the determined segment). Although Kornilov et al. disclose use of a mesh model (paragraph 82, the predetermined 3D face is a mesh model); Kornilov et al. do not expressly disclose generating a user mesh, the user mesh being representative of a face of the user based on the image data.
Chang et al., who also deal with 3D models, disclose a method for generating a user mesh, the user mesh being representative of a face of the user based on the image data (paragraph 54, the one or more of CPU 116 and GPU 118 may also perform operations that generate mesh data characterizing a mesh of the captured image based on the coefficient data, paragraph 86, Further, and at step 406, the image capture device 100 generates, based on the coefficient data, first mesh data characterizing a mesh of the image).
Kornilov et al. and Chang et al. are in the same field of endeavor, namely computer graphics.
Before the effective filing date of the claimed invention, it would have been obvious to apply the method of generating a user mesh, the user mesh being representative of a face of the user based on the image data, as taught by Chang et al., to the Kornilov et al. system, because this would represent the face model using a traditional, well-known data structure in computer graphics.
Claim(s) 4, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (U.S. PGPUB 20180096537) in view of Chang et al. (U.S. PGPUB 20230326136) and further in view of Moon et al. (U.S. Patent No. 9,317,785).
With respect to claim 4, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 3, wherein the reference mesh includes a plurality of nodes (Kornilov et al.: paragraph 42, the predetermined 3D face includes a predetermined set of points along the nose curve, Kornilov et al.: paragraph 82, Prior to the morphing, a known set of 3D points defines the shape of a particular facial feature on the predetermined 3D face), and wherein performing the rigid transformation includes performing a translation operation on a subset of the plurality of nodes of the reference mesh to fit the reference mesh to the user mesh (Kornilov et al.: paragraph 84, the internal eyebrow points of the predetermined 3D face are moved to match the locations of the internal eyebrow points of the 3D model of the user's face, the internal eye corners of the predetermined 3D face are moved to match the locations of the internal eye corners of the 3D model of the user's face, the external eye corners of the predetermined 3D face are moved to match the locations of the external eye corners of the 3D model of the user's face, and so forth). However, Kornilov et al. as modified by Chang et al. do not expressly disclose performing a rotation operation and a scaling operation on a subset of the plurality of nodes of the reference mesh to fit the reference mesh to the user mesh.
Moon et al., who also deal with 3D models, disclose a method for performing a rotation operation and a scaling operation on a subset of the plurality of nodes of the reference mesh to fit the reference mesh to the user mesh (column 10, lines 19-26, A generic face landmark shape model is then used, which is a mesh model that consists of edges and vertices for face landmark points including the corners of eyes, mouth, noses, and ears. Each vertex of the shape model has its own appearance model for detection. The generic face landmark shape model is scaled, rotated, and translated onto the faces on the input images based on the initially estimated face poses).
Kornilov et al., Chang et al., and Moon et al. are in the same field of endeavor, namely computer graphics.
Before the effective filing date of the claimed invention, it would have been obvious to apply the method of performing a rotation operation and a scaling operation on a subset of the plurality of nodes of the reference mesh to fit the reference mesh to the user mesh, as taught by Moon et al., to the Kornilov et al. as modified by Chang et al. system, because given the initial alignment of the face shape model, the best match between the face shape model and the face in an input image is found in such a way that the distance in shape and texture matching in terms of the face landmarks between two faces is minimized (column 10, lines 27-31 of Moon et al.).
With respect to claim 13, Kornilov et al. as modified by Chang et al. and Moon et al. disclose the non-transitory computer-readable medium of claim 12 for executing the method of claim 4; see rationale for rejection of claim 4.
With respect to claim 20, Kornilov et al. as modified by Chang et al. and Moon et al. disclose the system of claim 19, wherein the reference mesh includes a plurality of nodes (Kornilov et al.: paragraph 42, the predetermined 3D face includes a predetermined set of points along the nose curve, Kornilov et al.: paragraph 82, Prior to the morphing, a known set of 3D points defines the shape of a particular facial feature on the predetermined 3D face), and the user mesh includes a plurality of nodes (Kornilov et al.: paragraph 30, The plurality of 3D points in the 3D model of the user's face comprises reference points in 3D space that define the locations of various facial features (e.g., the two internal eyebrow points, the two internal eye corners, the two external eye corners, the two ear junctures, the two cheekbones, the nose tip) of the user), and wherein the instructions cause the at least one processor to project the reference mesh and the virtual frame onto the user mesh, including: perform a rigid transformation of the reference mesh and the virtual frame to the user mesh (Kornilov et al.: paragraph 89, As a result of morphing the predetermined 3D face, the previously selected 3D points along the nose curvature have been moved to new locations in 3D space (i.e., new 3D (x, y, z) coordinates)), including perform a rotation operation, a translation operation (Kornilov et al.: paragraph 84, the internal eyebrow points of the predetermined 3D face are moved to match the locations of the internal eyebrow points of the 3D model of the user's face, the internal eye corners of the predetermined 3D face are moved to match the locations of the internal eye corners of the 3D model of the user's face, the external eye corners of the predetermined 3D face are moved to match the locations of the external eye corners of the 3D model of the user's face, and so forth), and a scaling operation on a subset of the plurality of nodes of the reference mesh to fit the reference mesh to the user mesh (Moon et al.: column 10, lines 19-26, A generic face landmark shape model is then used, which is a mesh model that consists of edges and vertices for face landmark points including the corners of eyes, mouth, noses, and ears. Each vertex of the shape model has its own appearance model for detection. The generic face landmark shape model is scaled, rotated, and translated onto the faces on the input images based on the initially estimated face poses). Moon et al. disclose performing a rotation and scaling operation; see rationale for rejection of claim 4.
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (U.S. PGPUB 20180096537) in view of Chang et al. (U.S. PGPUB 20230326136) and further in view of Quinn et al. (U.S. PGPUB 20150123967).
With respect to claim 9, Kornilov et al. as modified by Chang et al. disclose the computer-implemented method of claim 1. However, Kornilov et al. as modified by Chang et al. do not expressly disclose selecting a reference mesh, from a plurality of reference meshes, including: detecting at least one facial landmark in the image data; mapping the at least one facial landmark to a corresponding node of the user mesh; selecting the reference mesh from the plurality of reference meshes based on relative positions indexing node of the user mesh and the node corresponding to the at least one facial landmark in the user mesh.
Quinn et al., who also deal with 3D models, disclose a method for selecting a reference mesh, from a plurality of reference meshes (paragraph 56, in step 216, selects a human head reference mesh model with N predefined 3D shape units based on the head size and head shape), including: detecting at least one facial landmark in the image data (paragraph 30, In step 101, a 3D image capture device (e.g. 20) captures 3D image data of head features including facial features of a user);
mapping the at least one facial landmark to a corresponding node of the user mesh (paragraph 30, in step 102, one or more software controlled processors of one or more computer systems communicatively coupled to the capture device generate a user 3D head model for the user based on the 3D image data);
selecting the reference mesh from the plurality of reference meshes based on relative positions indexing node of the user mesh and the node corresponding to the at least one facial landmark in the user mesh (paragraph 56, in step 216, selects a human head reference mesh model with N predefined 3D shape units based on the head size and head shape. In step 218, one or more corresponding points of head features in the 3D tracking mesh and the human head reference mesh model are identified. In step 220, software like the 3D facial recognition and reconstruction engine 116 executing on one or more processors iteratively performs a morph target animation technique operating on the shape units of the human head reference mesh model until a matching criteria with the tracking mesh is satisfied).
Kornilov et al., Chang et al., and Quinn et al. are in the same field of endeavor, namely computer graphics.
Before the effective filing date of the claimed invention, it would have been obvious to apply the method of selecting a reference mesh, from a plurality of reference meshes, including: detecting at least one facial landmark in the image data; mapping the at least one facial landmark to a corresponding node of the user mesh; selecting the reference mesh from the plurality of reference meshes based on relative positions indexing node of the user mesh and the node corresponding to the at least one facial landmark in the user mesh, as taught by Quinn et al., to the Kornilov et al. as modified by Chang et al. system, because this would implement automatically generating a facial avatar in a defined art style which resembles a user based on image data captured of the user and a predetermined set of transferable head (including facial) features associated with the defined art style (paragraph 22 of Quinn et al.), thus automatically selecting the head of avatar matching facial features.
With respect to claim 18, Kornilov et al. as modified by Chang et al. and Quinn et al. disclose the non-transitory computer-readable medium of claim 10, wherein the instructions cause the at least one processor to execute the method of claim 9; see rationale for rejection of claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW GUS YANG whose telephone number is (571)272-5514. The examiner can normally be reached M-F 9 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW G YANG/Primary Examiner, Art Unit 2614
9/17/26
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