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 Amendment
The Amendment filed October 3rd, 2025 has been entered. Claims 1-9, 11-17, 19 and 20 have been amended. Claims 1-20 remain rejected in the application. Applicant’s amendments to the claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed July 16th, 2025 and have therefore been withdrawn.
Specification
The disclosure is objected to because of the following informalities:
Page 19, paragraph 52, line 6, the word “to” should be moved between the words “close” and “a”, so that it reads “... the two vectors may be [[to]] close to a first value, ...”
Appropriate correction is required.
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 (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.
Claims 1, 4-6, 10-11, 14-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Pub. No.: US 2022/0284678 A1), hereinafter Chen, in view of Haeberling et al. (Pub. No.: US 2023/0316810 A1), hereinafter Haeberling, and further in view of Hwang et al.(U.S Patent: #9,928,647), hereinafter Hwang.
Regarding claim 1, Chen discloses a method (FIG. 1 and paragraph 28 teach that FIG. 1 is a flowchart illustrating a method of processing face information according to one or more embodiments of the present disclosure) comprising:
obtaining first data corresponding to a first three-dimensional (3D) face (Paragraph 61 teaches that at step S121, face parameter values of the first face image and face parameter values respectively corresponding to multiple second face images of the preset style are extracted, where the face parameter values include parameter values representing a face shape and parameter values representing a face expression and paragraph 50 teaches that illustratively, the dense point cloud data may represent a three-dimensional model of a face). However, Chen fails to disclose and one or more first landmark locations associated with the first 3D face.
Haeberling discloses and one or more first landmark locations associated with the first 3D face (Paragraph 100 teaches that the identifying a plurality of facial features can include identifying a first face associated with the at least one image... generating a first plurality of landmarks corresponding to the plurality of facial features on the first face, ... and associating a first location with each of the first plurality of landmarks). Since Chen teaches obtaining first data/location points corresponding to a three-dimensional (3D) face and Haeberling teaches obtaining data related to landmark locations corresponding to a face, it would have been obvious to a person having ordinary skill in the art to combine the features together so that in addition to obtaining a data type consisting of a location point on a three-dimensional face, location landmark data could then also be obtained as well.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen to incorporate the teachings of Haeberling, so that the combined features together would allow for more detailed location data of facial features to be acquired that include landmark locations as well.
Furthermore, Chen in view of Haeberling disclose obtaining second data corresponding to a second 3D face (Paragraph 61 of Chen teaches that at step S121, face parameter values of the first face image and face parameter values respectively corresponding to multiple second face images of the preset style are extracted);
performing at least one of one or more transformation processes associated with the first 3D face or one or more fitting processes associated with the first 3D face, to at least partially update a first shape of the first 3D face (Paragraph 47 of Chen teaches that illustratively, multiple second face images are pre-selected images having some features, which can be used to represent different first face images. For example, n second face images are selected, and for each first face image, the first face image can be represented by using the n second face images and linear fitting coefficients. Illustratively, to enable multiple second face images to represent most first face images in a fitting manner, images of faces having some prominent features over a mean/average face may be selected as the second face images and paragraphs 154-157 of Chen teach that in a possible implementation, the apparatus further includes an updating module 604, which is configured to: in response to a style update triggering operation, obtain dense point cloud data respectively corresponding to multiple second face images of a changed style; based on the first face image and the dense point cloud data respectively corresponding to the multiple second face images of the changed style, determine dense point cloud data of the first face image in the changed style; based on the dense point cloud data of the first face image in the changed style, generate a virtual face model of the first face image in the changed style.). However, Chen and Haeberling fail to disclose determining, based at least on the first shape of the first 3D shape as updated, an updated 3D face that includes a second shape that better represents the second 3D face as compared to the first shape of the first 3D shape.
Hwang discloses determining, based at least on the first shape of the first 3D shape as updated, an updated 3D face that includes a second shape that better represents the second 3D face as compared to the first shape of the first 3D shape (Col. 9, Lines 42-60 teach that the personalized 3D face model refiner 230 may refine a personalized 3D face model by comparing texture patterns of face images. The personalized 3D face model refiner 230 may compare texture patterns of face images using the personalized 3D face model, and may refine a shape of the personalized 3D face model to minimize or, alternatively, reduce a difference in texture patterns. The personalized 3D face model refiner 230 may project the personalized 3D face model to each of the first face image and the second face image. The personalized 3D face model refiner 230 may warp the personalized 3D face model to be matched to a face pose represented in each of face images, and may project the warped personalized 3D face model to the face images. The personalized 3D face model refiner 230 may refine the personalized 3D face model based on a difference in texture patterns between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected. Additionally, Col. 11, Lines 35-42 teach that the personalized 3D face model generating apparatus 200 may update a personalized 3D face model stored in the personalized 3D face model storage 240. When a new face image captured from the user face is input, the personalized 3D face model generating apparatus 200 may extract feature points of a face from the new face image and may update a previously generated personalized 3D face model based on the extracted feature points.). Since Chen in view of Haeberling teach obtaining data/location points corresponding to a first and second three-dimensional (3D) face and can perform update functions related to the shape of a face and Hwang teaches a 3D face model refiner that can update a 3D face shape by comparing a first face’s feature points to that of a second face’s feature points, it would have been obvious to a person having ordinary skill in the art to combine the features together so that any performed facial updates to a second face, could incorporate the features and shape of the first face, in order to improve and refine any potential shape changes between the two faces.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen in view of Haeberling to incorporate the teachings of Hwang, so that the combined features together would allow for more accurate and improved facial updates when determining facial features/shapes between a first 3D face and a second 3D face.
Furthermore, Chen in view of Haeberling and Hwang disclose determining one or more correspondences between one or more first points associated with the updated 3D face and one or more second points associated with the second 3D face (Col. 9, Line 61 through Col. 10, Line 3 of Hwang teach that the personalized 3D face model refiner 230 may extract a correspondence point between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected. The personalized 3D face model refiner 230 may compare a texture pattern of the first face image and a texture pattern of the second face image in a peripheral area of the correspondence point, and may refine the shape of the personalized 3D face model based on a result of comparing the texture patterns.);
determining, based at least on the one or more correspondences and using the one or more first landmark locations associated with the first 3D face, one or more second landmark locations associated with the second 3D face that corresponds to the one or more first landmark locations associated with the first 3D face (Paragraph 64 of Chen teaches that considering a correspondence between the face parameter values and the dense point cloud data for representing a same face, an association relationship between the first face image and the multiple second face images of the preset style may he determined according to the face parameter values respectively corresponding to the first face image and the multiple second face images, and then, according to the association relationship and the dense point cloud data respectively corresponding to the multiple second face images, the dense point cloud data of the first face image in the preset style is determined. Additionally, Col. 10, Lines 19-23 of Hwang teach that since an identical set of vertices of the personalized 3D face model is projected to each of face images, locations of correspondence points between the face images may be identified based on locations of the vertices of the personalized 3D face model projected to each of the face images and Col. 10, Lines 30-35 of Hwang teach that the personalized 3D face model refiner 230 may compare the texture pattern of the first face image and the texture pattern of the second face image in the peripheral area of the identified correspondence point, and may determine whether to adjust a location of a vertex of the personalized 3D face model corresponding to the correspondence point.);
and performing one or more animation operations with respect to the second 3D face based at least on the one or more second landmark locations (Paragraph 108 of Chen teaches that at step S301, in response to a style update triggering operation, dense point cloud data respectively corresponding to multiple second face images of a changed style is obtained).
Regarding claim 4, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Hwang disclose receiving input data indicating that a third landmark location associated with the first 3D face corresponds to a fourth landmark location associated with the second 3D face (Paragraph 72 of Chen teaches that illustratively, a large number of face images and the labeled face parameter values corresponding to each face image may be collected as the sample image set herein),
wherein the determining the one or more correspondences is further based at least on the input data (Paragraph 72 of Chen teaches that and each sample image is input into the to-be-trained neural network to obtain the predicted face parameter values corresponding to each sample image and output by the to-be-trained neural network).
Regarding claim 5, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Hwang disclose receiving input data indicating that one or more third landmark locations associated with the first 3D face correspond to one or more fourth landmark locations associated with the second 3D face (Paragraph 75 of Chen teaches that at step S1231, based on the face parameter values of the first face image and the face parameter values respectively corresponding to the multiple second face images of the preset style, linear fitting coefficients between the first face image and the multiple second face images of the preset style are determined);
and updating, based at least on the one or more third landmark locations corresponding to the one or more fourth landmark locations, one or more third points of the first 3D face that are associated with the one or more third landmark locations (Paragraph 79 of Chen teaches that in the embodiments of the present disclosure, it is proposed that linear fitting coefficients indicating an association relationship between the first face image and the multiple second face images of a preset style are obtained quickly by use of a smaller number of face parameter values, and further, the dense point cloud data of the multiple second face images of the preset style may be adjusted based on the linear fitting coefficients so as to quickly obtain the dense point cloud data of the first face image in the preset style),
wherein the determining the one or more correspondences is further based at least on the updating of the one or more third points (Paragraph 76 of Chen teaches that at step S1232, based on the dense point cloud data respectively corresponding to the multiple second face images of the preset style and the linear fitting coefficients, the dense point cloud data of the first face image in the preset style is determined).
Regarding claim 6, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Hwang disclose determining a first orientation associated with the first 3D face (Paragraph 57 of Chen teaches that after the dense point cloud data of the first face image in the preset style is determined, three-dimensional coordinate values of multiple vertices included in the input face in the pre-constructed three-dimensional coordinate system can be obtained, such that the virtual face model of the first face image in the preset style can be obtained based on the three-dimensional coordinate values of the multiple vertices in the three-dimensional coordinate system);
and determining, based at least on the first orientation, a second orientation associated with the second 3D face such that the second 3D face is substantially oriented with respect to the first 3D face (Paragraph 91 of Chen teaches that specifically, the dense point cloud data includes coordinate values of multiple corresponding dense points. For the above step S1232, based on the dense point cloud data respectively corresponding to multiple second face images of the preset style and the linear fitting coefficients, determining the dense point cloud data of the first face image under the preset style may include the following steps S12321 to S12324),
wherein the determining the one or more correspondences is further based at least on the first orientation and the second orientation (Paragraph 92 of Chen teaches that at step S12321, based on the coordinate values of the dense points respectively corresponding to the multiple second face images of the preset style, coordinate values of corresponding points in average dense point cloud data are determined).
Regarding claim 10, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Hwang disclose the one or more first landmark locations include one or more first locations of one or more facial features associated with the first 3D face (Paragraph 100 of Haeberling teaches that the identifying a plurality of facial features can include identifying a first face associated with the at least one image... generating a first plurality of landmarks corresponding to the plurality of facial features on the first face, ... and associating a first location with each of the first plurality of landmarks.);
and the one or more second landmark locations include one or more second locations of the one or more facial features associated with the second 3D face (Paragraph 100 of Haeberling teaches that the identifying a plurality of facial features can include ... identifying a second face associated with the at least one image, ... generating a second plurality of landmarks corresponding to the plurality of facial features on the second face, ... and associating a second location with each of the second plurality of landmarks).
Regarding claim 11, the system steps correspond to and are rejected similarly to the method steps of claim 1 (see claim 1 above). In addition, Chen discloses a system (FIG. 9 and paragraph 36 teach that FIG. 9 is a structural schematic diagram illustrating an apparatus for processing face information according to one or more embodiments of the present disclosure) comprising:
one or more processors (FIG. 10 and paragraph 167 teach that corresponding to the method of processing face information in FIG. 1, an embodiment of the present disclosure further provides an electronic device 700. As shown in FIG. 10, the electronic device 700 may include a processor 71, a memory 72 and a bus 73, The memory 72 is configured to store executable instructions and includes an internal memory 721 and an external memory 722. The internal memory 721 is also called internal storage device configured to temporarily store operational data of the processor 71 and data exchanged with the external memory 722 such as hard disk. The processor 71 exchanges data with the external memory 722 through the internal memory 721. When the electronic device 700 runs, the processor 71 communicates with the memory 72 via the bus 73 to perform the instructions.).
Regarding claim 14, the system steps correspond to and are rejected similarly to the method steps of claim 4 (see claim 4 above).
Regarding claim 15, the system steps correspond to and are rejected similarly to the method steps of claim 5 (see claim 5 above).
Regarding claim 16, the system steps correspond to and are rejected similarly to the method steps of claim 6 (see claim 6 above).
Regarding claim 18, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 11), in addition, Chen in view of Haeberling and Hwang disclose wherein the system is comprised in at least one of:
a system for performing deep learning operations (Paragraph 59 of Haeberling teaches that the neural renderer 250 may generate an intermediate representation of an object and/or scene, for example, that utilizes a neural network to render. Neural textures 244 may be used to jointly learn features on a texture map (e.g., feature map 240) along with a 5-layer U-Net, such as neural network 242 operating with neural renderer 250);
or a system for generating synthetic data (Paragraph 55 of Haeberling teaches that categories 234 may represent a classification for particular objects 236. For example, a category 234 may be eyeglasses and an object may be blue eyeglasses, clear eyeglasses, round eyeglasses, etc. Any category and object may be represented by the models described herein. The category 234 may be used as a basis in which to train generative models on objects 236. In some implementations, the category 234 may represent a dataset that can be used to synthetically render a 3D object category under different viewpoints giving access to a set of ground truth poses, color space images, and masks for multiple objects of the same category).
Regarding claim 19, the one or more processors correspond to and are rejected similarly to the method steps of claim 1 and the system steps of claim 11 (see claims 1 and 11 above).
Regarding claim 20, the one or more processors correspond to and are rejected similarly to the system steps of claim 18 (see claim 18 above).
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Haeberling and Hwang, as applied to claims 1 and 11 above, and further in view of Lin et al. (Pub. No.: US 2022/0044491 A1), hereinafter Lin.
Regarding claim 2, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), however, Chen in view of Haeberling and Hwang fail to disclose wherein the performing the at least one of the one or more transformation processes associated with the first 3D face or the one or more fitting processes associated with the first 3D face comprises:
performing a transformation process of the one or more transformation processes by at least updating at least one of a rotation, a translation, or a scale associated with the first 3D face.
Lin discloses performing a transformation process of the one or more transformation processes by at least updating at least one of a rotation, a translation, or a scale associated with the first 3D face (Paragraph 78 of Lin teaches that the first posture parameter includes at least one of a rotation parameter, a translation parameter, and a scaling parameter. The first posture parameter is solved according to the formula corresponding to alignment of the three-dimensional face mesh with the first three-dimensional face model). Since Chen in view of Haeberling and Hwang teach an initial fitting process and Lin teaches a transformation process that allows for the use of applying a rotation, translation or scaling of a three-dimensional face model, it would have been obvious to a person having ordinary skill in the art to combine the features together so that in addition to being able to make fitting adjustments to the three-dimensional face model, transformation adjustments of updating a rotation, translation, or scale associated with the 3D face could also be implemented.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen in view of Haeberling and Hwang to incorporate the teachings of Lin, so that the combined features together would for more in depth transformation and fitting processes by being able to additionally make adjustments of the rotation, translation and scale of the 3D face model.
Furthermore, Chen in view of Haeberling, Hwang and Lin disclose and performing a fitting process of the one or more fitting processes by at least updating a shape of the first 3D face (Paragraph 77 of Lin teaches that in Step 7041: Perform fitting on the three-dimensional face mesh and the local area of the first three-dimensional face model according to the first correspondence, to calculate a first posture parameter of the second three-dimensional face model).
Regarding claim 3, Chen in view of Haeberling, Hwang and Lin disclose everything claimed as applied above (see claim 2), in addition, Chen in view of Haeberling, Hwang and Lin disclose wherein the performing the fitting process occurs after the performing the transformation process, and wherein the performing the at least one of the one or more transformation processes associated with the first 3D face or the one or more fitting processes associated with the first 3D face further comprises:
after the performing the fitting process, performing a second transformation process of the one or more transformation processes by at least further updating at least one of the rotation, the translation, or the scale associated with the first 3D face (Paragraph 103 of Lin teaches that in step 7062: Adjust a shape base coefficient of the second three-dimensional face model according to the second posture parameter, to obtain a shape base coefficient of the three-dimensional face model of the target object after global fitting);
and after the performing the second transformation process, performing a second fitting process by at least further updating the shape of the first 3D face (Paragraph 101 of Lin teaches that in step 7061: Perform fitting on the three-dimensional face mesh and the global area of the second three-dimensional face model according to the second correspondence, to calculate a second posture parameter of the three-dimensional face model of the target object after global fitting).
Regarding claim 12, the system steps correspond to and are rejected similarly to the method steps of claim 2 (see claim 2 above).
Regarding claim 13, the system steps correspond to and are rejected similarly to the method steps of claim 3 (see claim 3 above).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Haeberling and Hwang, as applied to claim 1 above, and further in view of Guo et al. (Pub. No.: US 2021/0312685 A1), hereinafter Guo.
Regarding claim 7, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), however, Chen in view of Haeberling and Hwang fail to disclose determining a first bounding shape associated with the first 3D face.
Guo discloses determining a first bounding shape associated with the first 3D face (Paragraph 34 teaches that at block 102, a first face key point of the face of the virtual object ... are extracted and paragraph 35 teaches that the first face key point ... may be understood as key points of the corresponding face region, which may include a plurality of key points that may define a contour of a face shape such as a canthus, a tip of a nose, corners of a mouth, a chin). Since Chen in view of Haeberling and Hwang teach the initial method steps of determining facial data related to a first 3D face and Guo teaches using key points that can be associated as facial regions, which are similar to a bounding box, it would have been obvious to a person having ordinary skill in the art to combine the features together so that in addition to determining first data points and landmark locations associated with a first 3D face, a facial region (or bounding box) of multiple points and landmark locations could be used as well.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen in view of Haeberling and Hwang to incorporate the teachings of Guo, so that the combined features together would allow for additional groups of data points and locations to be determined as a facial region or in a bounding box.
Furthermore, Chen in view of Haeberling, Hwang and Guo disclose determining a second bounding shape associated with the second 3D face (Paragraph 34 of Guo teaches that at block 102, ... a second face key point of each of the original face images are extracted and paragraph 35 of Guo teaches that the second face key point may be understood as key points of the corresponding face region, which may include a plurality of key points that may define a contour of a face shape such as a canthus, a tip of a nose, corners of a mouth, a chin);
and aligning, based at least on the first bounding shape and the second bounding shape (Paragraph 64 of Guo teaches that in some embodiments, the vertex information of each second 3D face is controlled to make position adjustment based on the position and the posture information of the first 3D face, thus the target face image corresponding to each original face image is generated, and the posture and position of the target face image have been aligned with the figure image of the corresponding virtual object),
the first 3D face with respect to the second 3D face, wherein the determining the correspondence is further based at least on the aligning of the first 3D shape with respect to the second 3D shape (Paragraph 78 of Guo teaches that at block 901, each second face key point is processed based on a preset algorithm, to generate a position and posture information of the second 3D face corresponding to each original face image).
Claim 8-9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Haeberling and Hwang, as applied to claims 1 and 11 above, and further in view of Li et al. (Pub. No.: US 2022/0222893 A1), hereinafter Li.
Regarding claim 8, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Hwang disclose wherein the determining the one or more second landmark locations associated with the second 3D face comprises:
determining, based at least on the correspondence and using a first landmark location of the one or more first landmark locations, a potential landmark location associated with the second 3D face (Paragraph 91 of Haeberling teaches that in step S515 facial features are identified. For example, features associated with the detected face(s) can be identified. The facial features can be extracted using a 2D ML algorithm or model. The facial features extractor can be implemented as a function call in a software application. The function call can return the location of facial landmarks (or key points) of a face. The facial landmarks can include eyes, mouth, ears, and/or the like).
However, Chen in view of Haeberling and Hwang fail to disclose determining a first surface normal angle associated with the first landmark location and a second surface normal location associated with the potential landmark location.
Li discloses determining a first surface normal angle associated with the first landmark location and a second surface normal angle associated with the potential landmark location (Paragraph 76 teaches that because coordinates of the at least one second marker point in the first face image and the second face image are different, according to the coordinates of the at least one second marker point in the first face image and the coordinates of the at least one second marker point in the second face image, a rotation and translation matrix used for converting the coordinates of the at least one second marker point in the first face image into the coordinates of the at least one second marker point in the second face image can be determined, or, a rotation and translation matrix used for converting the coordinates of the at least one second marker point in the second face image into the coordinates of the at least one second marker point in the first face image can be determined. The rotation and translation matrix is converted into an angle, and the angle is used as the posture angle difference between the second face image and the first face image). Since Chen in view of Haeberling teach the initial method steps for determining potential landmark locations associated with different 3D faces and Li teaches determining an angle based on the posture differences between a first face and second face, it would have been obvious to a person having ordinary skill in the art to combine the features together so that the direction of the different landmark locations between the two faces could be taken into account and a normal surface angle between the landmark locations could then be determined.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen in view of Haeberling to incorporate the teachings of Li, so that the combined features together would allow for additional landmark location data, including angles and directions between the locations, to be able to be utilized in improving the determining of other potential landmark locations on each of the faces.
Furthermore, Chen in view of Haeberling and Li disclose determining that the first surface normal angle is within a threshold angle to the second surface normal angle (FIG. 15 and paragraphs 160-161 of Li teach that in some embodiments, as shown in FIG. 15, the apparatus further includes: a threshold determining module 1409 and paragraph 82 of Li teaches that step 2033. Select a second face image with a largest posture angle difference from each second image sequence as the target face image);
and based at least on the first surface normal angle being within the threshold angle to the second surface normal angle, determining that the potential landmark location includes a second landmark location of the one or more second landmark locations (Paragraph 83 of Li teaches that because the first face image to the front face type, the second face image to another image type such as the left face type or the right face type, a larger posture angle difference between the second face image and the first face image indicates that a region that matches the image type of the second face image and that is included in the second face image is more complete, and the subsequently generated three-dimensional face model is more accurate).
Regarding claim 9, Chen in view of Haeberling and Hwang disclose everything claimed as applied above (see claim 1), however, Chen in view of Haeberling and Hwang fail to disclose wherein the performing the at least one of the one or more transformation processes associated with the first 3D face or the one or more fitting processes associated with the first 3D face uses at least one of:
one or more distances between one or more first points associated with the first 3D face and one or more second points associated with the second 3D face;
or one or more first surface normal angles associated with the one or more first points and one or more second surface normal angles associated with the one or more second points.
Li discloses one or more first surface normal angles associated with the one or more first points and one or more second surface normal angles associated with the one or more second points (Paragraph 83 teaches that a face region corresponding to the left face type is the left face, posture angle differences between two second face images of the left face type and the first face image are respectively 20 degrees and 30 degrees, and the left face displayed in the second face image with the posture angle difference of 30 degrees is more complete than the left face displayed in the second face image with the posture angle difference of 20 degrees). Since Chen in view of Haeberling and Hwang teach the initial method steps of performing a fitting process that is associated with the first 3D face and Li teaches a process for being able to determine the differences between different angles associated to a face region containing facial points, it would have been obvious to a person having ordinary skill in the art to combine the features together so that any direction or angle associated between different facial points of a 3D face, could be utilized to help in improving the accuracy of the fitting process.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen in view of Haeberling and Hwang to incorporate the teachings of Li, so that the combined features together would improve the overall accuracy of the fitting process by incorporating angles and directions of associated points on a 3D face.
Regarding claim 17, the system steps correspond to and are rejected similarly to the method steps of claim 8 (see claim 8 above).
Response to Arguments
Applicant’s arguments with respect to claims 1, 11 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The prior art of Hwang has been incorporated into the rejection of the independent claims and therefore teaches the newly amended claim language (See claim 1 above).
In regards to the additional arguments regarding any of the dependent claims 2-10, 12-18 and 20, for the virtue of their dependency are moot because the independent claims are not allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Miao (U.S. Patent: #10,672,195 B2) an information processing method and device for calculating at least one shape parameter and an expression parameter based on a correspondence relationship
Wang et al. (U.S. Patent: #11,475,624 B2) teaches a method and apparatus for generating three-dimensional face models.
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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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached at (571) 272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.R./Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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