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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 3rd, 2026 has been entered.
Response to Amendment
The Amendment filed March 3rd, 2026 has been entered. Claims 1, 7, 11, 13, 17 and 19 have been amended. Claims 1-20 remain pending and rejected in the application. Applicant’s amendments to the specifications have overcome each and every objection previously set forth in the Non-Final Office Action mailed January 23rd, 2026 and have therefore been withdrawn.
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 Singhal has been incorporated into the rejection of the independent claims and therefore teaches the newly amended claim language (See claim 1 below).
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.
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, 4-7, 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 Singhal et al.(Pub. No.: US 2018/0114546 A1), hereinafter Singhal.
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 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 on the first 3D face.
Haeberling discloses and one or more first landmark locations on 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 determining, based on 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 in view of Haeberling fail to disclose an updated 3D face that includes a second shape that better represents a second 3D face as compared to the first shape of the first 3D face.
Singhal discloses an updated 3D face that includes a second shape that better represents a second 3D face as compared to the first shape of the first 3D face (FIGS 7A-8B and paragraph 44 teach that FIGS. 7A-8B show additional examples of the preview subject automatically editing the target subject's facial expression via UI 100 and various embodiments of the processes and methods discussed herein. Thus a comparison of the preview image data and the updated target image data depicted in FIGS. 1B and 7A-8B, show various examples of the results of editing the target face based on variations of the preview face, provided via a live camera feed and paragraph 45 teaches that FIG. 8A illustrates the user interface 100 of FIG. 1A, wherein the facial expression of the target subject 140 has been edited to include a distorted shape of the mouth 144 based on current preview image data depicting the preview subject 120 distorting the shape of their mouth 124. FIG. 8B illustrates the user interface 100 of FIG. 1A, wherein the facial expression of the target subject 140 has been edited to include another distorted shape of the mouth 144 based on current preview image data depicting the preview subject 120 distorting the shape of their mouth 124.). Since Chen in view of Haeberling teach obtaining data/landmark points corresponding to and located on a three-dimensional (3D) face and the ability to update and transform a virtual 3D face model and Singhal teaches the ability to update different shapes of a first 3D face model in reference to second, different, 3D face model data, it would have been obvious to a person having ordinary skill in the art to combine the features together so that any updating to a 3D face model/image, could be compared with and use a second related 3D face model/image to perform associated shape updates based on a corresponding relationship to that second 3D face.
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 Singhal, so that the combined features together would allow for more accurate and realistic looking 3D model face updates by using a second 3D face model for shape references to correspond with.
Furthermore, Chen in view of Haeberling and Singhal 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 (Paragraph 70 of Singhal teaches that more particularly, landmark points may be spatial points (or approximations of physical points). Each landmark point in the model corresponds to a point, pixel, location, index, address, or the like included in the image data. Although the various embodiments discussed herein include two-dimensional (2D) image data, other embodiments are not so constrained. For instance, it should be understood that at least some of the various embodiments discussed herein may be extended to three-dimensional (3D) image data, such as in the context of VR, AR, or other such applications. One or more landmark points may correspond to facial features, regions, areas, or locations within the face of the image data. Additionally, paragraph 96 of Singhal teaches that in some embodiments, the updated image data for the target face may be displayed. For instance, each of the corresponding target display windows in UI 100 in FIGS. 1B and 7A-8B shows updated image data for the target face. Note the correspondence and/or correlations between the varied features of the facial expression of the preview subject and the edited and/or updated features of the facial expression of the target subject.);
determining that the one or more first points associated with the updated 3D face correspond to the one or more first landmark locations on the first 3D face (Paragraph 86 of Singhal teaches that furthermore, an isomorphism between the reference/current models for the preview face and the face model for the target face may be determined at block 404. For instance, each face model may include the same number of landmark points N, where N is a positive integer. A one-to-one mapping or correspondence between each of the N landmark points of the face models for the preview face and each of the N landmark points of the face model for the target face may be determined at block 404.);
determining, based at least on the one or more correspondences and the one or more first points corresponding to the one or more first landmark locations on the first 3D face, one or more second landmark locations on the second 3D face that correspond to the one or more first landmark locations on the first 3D face (Paragraph 107 of Singhal teaches that at block 432, an isomorphism between the current face model for the preview face and the face model for the target face is determined. In at least one embodiment, the isomorphism determined at block 432 is additionally between reference face model for the preview face and the current face model for the preview face. For instance, a one-to-one mapping and/or correspondence between each of the N landmark points of the current face model of the preview face and each of the N landmark points of the face model for the target face is determined. Furthermore, a one-to-one mapping, correlation, association, and/or correspondence between each of the N landmark points of the current face model of the preview face and each of the N landmark points of the reference face model for the preview face may be determined.);
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 Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal 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 Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal 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 Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal 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 7, Chen in view of Haeberling and Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal disclose wherein: the one or more first landmark locations on the first 3D face are at one or more third points on the first 3D face (Paragraph 104 of Singhal teaches that at block 426, a reference face model for the preview face is generated based on the reference image data of the preview face. The generated reference face model for the preview face may be a mesh face model. In some embodiments, the reference face model for the preview face may include N landmark points, where N is a positive integer greater than 2. N may take on virtually any integer greater than 2.);
and the determining that the one or more first points associated with the updated 3D face correspond to the one or more first landmark locations on the first 3D face comprises determining that the one or more first points associated with the updated 3D face correspond to the one or more third points associated with the first 3D face (Paragraph 110 of Singhal teaches that at block 444, the updated current face model for the preview face is compared to reference face model for the preview face. Various embodiments for comparing the current and reference face models for the preview face are discussed in conjunction with at least processes 560 and 580 of FIGS. 5C and 5D respectively, as well as pseudo-code 600 of FIG. 6. However, briefly, a comparison between the updated current and the reference face models of the preview face is generated at block 444. The comparison may be based on the isomorphism between the current and reference face models. The comparison may generate one or more spatial displacements associated with each of the N landmark points included in the current face model, as compared to the reference face model.).
Regarding claim 10, Chen in view of Haeberling and Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal 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 Singhal disclose everything claimed as applied above (see claim 11), in addition, Chen in view of Haeberling and Singhal 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 Singhal, 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 Singhal disclose everything claimed as applied above (see claim 1), however, Chen in view of Haeberling and Singhal 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 Singhal 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 Singhal 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, Singhal 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, Singhal and Lin disclose everything claimed as applied above (see claim 2), in addition, Chen in view of Haeberling, Singhal 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).
Claims 8-9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Haeberling and Singhal, 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 Singhal disclose everything claimed as applied above (see claim 1), in addition, Chen in view of Haeberling and Singhal 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 Singhal 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 Singhal disclose everything claimed as applied above (see claim 1), however, Chen in view of Haeberling and Singhal 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 Singhal 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 Singhal 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kuta et al. (Pub. No.: US 2022/0020197 A1) teaches a system and method for animating an image of an object by extracting a plurality of three dimensional (3D) features from a first image and a second image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to George Renze whose telephone number is (703)756-5811. The examiner can normally be reached Monday-Friday 9:00am - 6:00pm EST.
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
Prosecution Insights