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 6/05/2026 has been entered. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant’s arguments, see pages 9-11 of Arguments/Remarks, filed 6/05/2026, with respect to the rejection(s) of claims 1, 9, and 17, under 102(a)(2) have been fully considered and are persuasive. Therefore, the 35 U.S.C. 102(a)(2) rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Bhat (US 10198845 B1).
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, 8-12, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mann (US 11830159 B1) in view of Bhat (US 10198845 B1).
Regarding claims 9, 1, 17,
Mann teaches:
An electronic device, comprising: one or more processors; and a storage device configured to store one or more programs; wherein the one or more programs, when executed by the one or more processors, (Mann Col. 10 Line 31 “The memory 102 is communicatively coupled to processing circuitry 104, which may include any number of processing units such as a central processing unit (CPU), a graphics processing unit (GPU)”) cause the one or more processors to implement acts comprising: obtaining audio data and target part data corresponding to a target object in a plurality of video frames, (Col. 1 Line 19 “A typical filmmaking process involves a production phase spread over 20 multiple shoots, in which raw video footage is captured (along with audio)” Col. 5 Line 21 “The object may be a human face, and obtaining the current values of the set of adjustable parameters may be based on an audio track containing speech.” Note: Mann teaches that for a target object, in this case a human face, corresponding audio can be captured along with the data of the object. As clarified by Mann, the video and audio capture spans over multiple shoots, thus teaching that there are a plurality of video frames depicting the target object.) wherein the target part data corresponds to position information and state information of a predetermined capture part and the target part data comprises mesh point data;(Col. 12 Line 51 “a particular instance of an object may be determined for example by processing the relevant portions of the input video data 114 using an object tracker. An object tracker may for example detect and localize an instance of an object within the input video data 114 and determine locations (in two or three dimensions) of predetermined key points on the object, which may be matched with locations of corresponding key points on the object model. Different object trackers may be used to determine values of adjustable parameters … parameters for example encoding adjustments applied to the base geometry of the model, thereby causing the model to exhibit different appearances, for example different facial expressions and mouth movements” Note: Mann teaches particular instances of objects can be determined from certain parts of video data, teaching that the instance/state of an object is data used by Mann. This is done by an object tracker which will also map key points on the object to store location/position information. Using this info the object tracker can store parameters defining target part data, meaning data for specific parts of the object such as the mouth. As Mann teaches a model of the object is made with specific key points identified, Mann teaches that the target part data includes mesh point data.) determining first to-be-fused data corresponding to the audio data; (Col. 22 Line 64 “The primary dialogue audio 302 and (optionally) the primary dialogue text 308 are processed using a facial animation model 312. The facial animation model 312 is arranged to determine values of the adjustable parameters of the object representation model corresponding to the input audio … The facial animation model 312 may for example process input audio and/or input text to determine a time-aligned viseme sequence.” Note: From the audio data, corresponding data that will be fused is determined, in this case a viseme (mouth movement) sequence data.) determining second to-be-fused data corresponding to the target part data; and determining target fusion data based on the first to-be-fused data and the second to-be-fused data; driving the display of the target virtual object based on the target fusion data. (Col. 23 line 29 “In the present instance, the primary dialogue audio 302 and/or the primary dialogue text 308 are processed using the facial animation model 312 to determine a sequence of primary blendshapes 314 encoding estimated facial deformations corresponding to the primary dialogue … In addition to the primary blendshapes 314 corresponding to the primary dialogue …, a sequence of base blendshapes 320 is determined, corresponding to the appearance of the actor's face in the input video 318, along with optionally values of other adjustable parameters for reproducing the appearance of the actor's face within the input video 318 … The method of FIG. 3 continues with performance transfer 322, in which the base blendshapes 320, the primary blendshapes 314 … are combined or otherwise processed to determine a sequence of combined blendshapes … Following the determination of the sequence of combined blendshapes 324, rendering 326 may be performed in which the combined blendshapes (and other adjustable parameter values) are provided to the object representation model, thereby to generate a video layer 328 comprising an animated representation of the actor's face, or a portion of the actor's face such as a jaw portion containing the mouth” Note: Mann teaches that the input video and adjustable parameters are used to produce a base blend shape, it has already been taught that the input video captures the whole object, a face, and that the parameters are target part data detailing data for specific parts like the mouth. This data is combined, or fused, with the primary blendshapes, 3D object data of the mouth derived from the audio capture, to produce a final combined virtual object of the face.) and updating the target fusion data (Mann Col. 23 Line 29, cited above, teaches that the target fusion data can be updated by combining, or fusing, the blendshapes with the primary blendshapes to obtain updated target fusion data.) and driving the display of the target virtual object based on the updated target fusion data (Mann Col. 23 Line 43 “Following the determination of the sequence of combined blendshapes 324, rendering 326 may be performed in which the combined blendshapes (and other adjustable parameter values) are provided to the object representation model, thereby to generate a video layer 328 comprising an animated representation of the actor's face, or a portion of the actor's face such as a jaw portion containing the mouth but excluding the eyes. Compositing 330 may then be performed in which the video layer 328 s composited with the input video 318 to generate composite video data 332, which may then be displayed via a user interface” Note: Mann teaches the target virtual object based on the updated target fusion data is displayed. The target virtual object is the animated representation of the actor’s face obtained from the combined blendshapes, aka the updated target fusion data, and is composited with the input video to display via a user interface.) and updating the target fusion data based on the to-be-superimposed fusion data. (Mann Col. 24 Line 2 “Subtracting the primary blendshape from the base blendshape may have the effect of removing or reducing the mouth deformation from the actor's original performance, whilst retaining other aspects of the actor's expression and performance … The resulting sequence of combined blendshapes 324 may therefore represent the actor of the input video 318 exhibiting mouth movements corresponding to the sequence of visemes of the secondary dialogue, whilst retaining expressiveness and other aspects of the performance from the input video 318. Although in the present example the performance transfer 322 involves determining a linear combination of blendshapes, in other examples alternative or additional processing may be performed.” Note: Mann teaches that in its process of overlaying, or superimposing, its target fusion data for the mouth to the “to-be-superimposed” base fusion data the mouth movement will now lack the expressiveness of what the original face is doing. To remedy this discrepancy the target fusion data is updated based on the to-be-superimposed fusion data, meaning the new mouth data, maintains its different animation but now also moves to match the facial expressions being made.)
Mann does not however detail a fusion curve that uses fusion data to adjust the target mesh point data where fusion values correspond to different predetermined facial expressions to update the target fusion data. This is taught by Baht which teaches wherein updating the target fusion data comprises: determining to-be-superimposed fusion data of a target mesh point by adjusting the target mesh point data (Bhat Col. 11 Line 46 “Morph targets in accordance with many embodiments of the invention include base shapes that represent the movements of the individual action units as well as corrective shapes that represent the movements of two or more action units. In the sample of FIG. 12, arrows indicate the direction and magnitude of various action units. In some embodiments, morph targets (including base shapes and corrective shapes) are stored in a standardized model file format. In certain embodiments, the facial rig is embedded using a modified version of the GL transmission format (gITF), a specification for 3D models.”
Col. 6 Line 37 “Mapping engines in accordance with a number of embodiments of the invention can animate expressions of a 3D model by morphing between the morph targets of a 3D model based on the weights calculated by weighting engines. In some embodiments, the morphing is a linear combination of the morph targets with their corresponding weights.”
Col. 9 Line 27 “In some embodiments, function curves are used to control the rate of blending weights to morph between different expressions. Function curves in accordance with a number of embodiments of the invention can be used to ensure smooth blending of the morph target weights between detected expressions. In certain embodiments, it can be desirable to provide a fast ramp in of a current detected expression, and a fast blend out of the previous expression (or neutral). In many embodiments, a function curve used for blending into an expression can be different from a function curve for blending out of an expression, providing different rates of change for smoother and more natural transitions. Example function curves for expression blending are illustrated in FIGS. 8-9. In FIG. 8, an example function curve for ramping into an expression is illustrated. The function curve of this figure is used to modify a morph target corresponding to a “detected” expression, allowing the morph target weight to ramp up very quickly to a full expression (at 1.0). The morph target weight then remains at 1.0, as the solved weight increases past the threshold (set to 0.4 in this example).
Note: The claims “target mesh point data” refers to the point data that makes up a 3D model/mesh for a target object, in this case a human face. Bhat teaches this same concept as its morph targets are base shapes, or 3D models, for a human face, even specifying that its 3D model is stored in the common GL transmission format. Thus, Bhat teaches target mesh point data, which it teaches is fused with other facial expressions to transition and move between them. Bhat teaches this process is dictated by the ‘function curve’ which handles blending/fusing and does so with specific blending weights. As Bhat teaches that this data can be blended/fused with other morph targets based on a function curve which blends them, Bhat teaches determining target mesh point based on a fusion curve.) according to a predetermined fusion curve,(Col. 9 line 40 “Example function curves for expression blending are illustrated in FIGS. 8-9. In FIG. 8, an example function curve for ramping into an expression is illustrated. The function curve of this figure is used to modify a morph target corresponding to a “detected” expression, allowing the morph target weight to ramp up very quickly to a full expression (at 1.0). The morph target weight then remains at 1.0, as the solved weight increases past the threshold (set to 0.4 in this example).”
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Note: Bhat teaches that its function curves help to fuse/blend between one facial expression into the next. Examples of this function/fusion curve can be seen in Fig. 8 and 9. Bhat Col. 7 line 49, cited below, teaches that the weights, which the curve transitions between, are “predefined weights for various emotions, expressions of different 3D models”. Thus, the fusion curves in Fig. 8 and 9 between two weights is a predefined curve as it simply expressed a relationship between two predefined values. Furthermore, when using the fusion curve Bhat teaches that it can be used directly without first having to perform a calculation or other step, teaching that the curve must be ready for use, in other words predetermined, for the model to use.) wherein the fusion curve comprises fusion values corresponding to different predetermined facial expressions at respective mesh points (Bhat Col. 7 Line 49 “By classifying a user's expression and using predefined weights for various emotions, expressions of different 3D models (or avatars) can be standardized, allowing a designer to create morph targets of a model based on predictable weightings for various expressions, such as (but not limited to) a happy face, a surprised face, a sad face, etc. Each expression may include multiple weights for various different morph targets in a 3D model.” “Once an expression has been classified, morph target weighting generators in accordance with many embodiments of the invention identify predefined weights for the classified expression. In some embodiments, morph target weighting generators use a combination of the predefined weights and the weights calculated by the numerical solver to determine a final set of weights for the morph targets. By classifying a user's expression and using predefined weights for various emotions, expressions of different 3D models (or avatars) can be standardized, allowing a designer to create morph targets of a model based on predictable weightings for various expressions, such as (but not limited to) a happy face, a surprised face, a sad face, etc.”
Note: Bhat Col. 9 Line 27, cited previously, teaches that the blending between morph targets, which are 3D models/meshes of different facial expressions, is performed by a function curve, the claims fusion curve, where the function curve uses blend weights to perform the fusion. These blend weights of the function curve are the claims “fusion values corresponding to different predetermined facial expressions at respective mesh points”, as the blend weights are specifically for facial expressions like “happy face, a surprised face, a sad face” where the weight(s) are tied specifically to morph targets, aka 3D models. As these 3D meshes/models must implicitly have mesh point data, and are associated with the predefined blending weights/fusion values, Bhat teaches a fusion curve comprising fusion values that correspond to different predetermined facial expressions at respective mesh points.) and the fusion values are updated according to the mesh point data of a predetermined number of initial video frames of the plurality of video frames; (Bhat Col. 10 Line 7 “An example of a process for identifying an initial neutral frame for a user in accordance with an embodiment of the invention is described below with reference to FIG. 10. In order to identify a neutral frame, process 1000 detects (1005) a user and identifies (1010) landmarks of an initial set of images of the user. In several embodiments, the identified facial landmarks from images of a user's face are fitted to a 3D template based on a distribution of the 3D points.”
Col. 9 Line 63 “The framework in accordance with a number of embodiments of the invention builds an internal 3D representation of the users face in neutral expression (or neutral frame) by combining facial landmarks across multiple frames of video. In several embodiments, neutral frames are computed by a numerical solver and are continually updated over time with additional frames of video. Neutral frames in accordance with many embodiments of the invention are estimated in multiple stages: at the very first frame of video, and subsequently by accumulating multiple frames that are classified as “neutral” by the expression classifier.”
Col. 7 Line 5 “In several embodiments, numerical solvers use a neutral state shape in the optimization and update the neural state shape as the system sees more images of the users face in a video sequence. Neutral shapes can be used in conjunction with subsequent images of a user's face to more accurately classify a user's expression.”
Col. 9 Line 17 “The final weight in accordance with several embodiments of the invention can be computed via a non-linear blend function that combines the predefined weights (on a subset of the morph target shapes) with the solved weights from the numerical solver.”
Col. 10 Line 36 “When process 1000 determines (1020) that the expression state is neutral, the process sets (1025) the identified landmarks of the initial image set as the initial neutral frame. When process 1000 determines (1020) that the expression state is not neutral, the process attempts to estimate (1030) an initial frame … For example, in certain embodiments, if a user is determined to be smiling from an initial set of images, neutral frames can be calculated to adjust landmark positions of the neutral frame by reversing the expected changes of a smile from a neutral position based on neutral frames from other models.”
Note: Bhat teaches that from its video capture input of frames/images it will evaluate certain images are of an “initial set of images”. It attempts to use these initial images/frames to define a model for a neutral facial expression. Bhat specifically teaches that the fusion values, which are the blending weights, can be adjusted based on weights from the “numerical solver”, where the numerical solver leverages the neutral state to help optimize the final weights. As the neutral state is info specifically about neutral frames which are an initial set of images/frames and that info is used to adjust the fusion weights, Bhat teaches using the initial frames to update the fusion values.)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Mann with Bhat where updating target fusion data based on to-be-superimposed data where: determining the to-be-super imposed fusion data of a target mesh point data is done by adjusting the data according to a predetermined fusion curve that leverages fusion values that can be updated according to the mesh point data of initial frames.
There are several reasons that would motivate one to do so, when fusing 3D models in a context similar to Bhat’s, Mann’s, and the applications where the model is fused with another model of the same object, like fusing two different expressions of the same face, it is common to perform the same fusion step multiple times as the face changes. Rather than having to recompute how fusion should be handled every time one expression’s target mesh point data is fused with another’s a predetermined fusion curve could be leveraged, where the system could simply refer to the curve rather than having to handle fusing from scratch.
Regarding claim 10, 2, 18
Mann teaches:
The device of claim 9, wherein obtaining the audio data and the target part data corresponding to the target object comprises: in response to a virtual object driving operation, capturing audio information corresponding to the target object based on an audio capture device; and capturing the target part data corresponding to the target object based on a face image capture device, wherein a part corresponding to the target part data comprises at least one part of the five sense organs. (Col. 24 line 19 “For example, a sequence of target blendshapes may be determined based on the base blendshapes 320, the primary blendshapes 314 … and automatic interpolation between the base blendshapes 320 and the target blendshapes may be performed, for example in dependence on the primary dialogue audio … In one example, breaks in speech may be identified from the audio track(s), for example using a machine learning model or audio filter, and interpolation may be performed so that the actor's mouth shape moves according to the base blendshapes 320 when the actor is not speaking, and according to the target blendshapes when the actor is speaking” Note: Mann teaches that blendshapes for a specific target area found in the primary blendshapes, referring to data obtained from the audio track, and base blendshapes, obtained from the video capture and parameters that focus on specific target areas, can be combined to make a virtual object. Mann directly teaches that the target part found in the video and audio data can be a sense organ, in this case the mouth.)
Regarding claims 11, 3, 19,
Mann teaches:
The device of claim 9, wherein determining the first to-be-fused data corresponding to the audio data comprises: determining a plurality of pieces of text information corresponding to the audio data, and determining pronunciation information corresponding to the plurality of pieces of text information; and determining first to-be-fused data of a mouth part based on the pronunciation information. (Mann Col. 6 Line 4 “Determining the first sequences of visemes may include processing the first audio track and a text representation of the original speech” Col. 23 Line 14 “For example, the facial animation model may be a trainable model provided with a language-dependent pronunciation dictionary …The facial animation model 312 may first generate a phoneme sequence based on the input audio and/or input text and then determine a corresponding viseme sequence from the generated phoneme sequence. The determined viseme sequence may then be converted to a sequence of adjustable parameter values such as blendshapes (e.g. delta blendshapes corresponding to mouth deformations) based on a viseme-blendshape mapping.” Note: Mann teaches that audio directly and/or corresponding text of the audio can be input to a facial animation model which contains a pronunciation dictionary to derive phoneme sequences from. Using these a viseme, or lip/mouth formation, sequence can be derived and used to make blendshapes. These blendshapes are the same ones described in the previous citation Col. 24 Line 19 which describes how they can be used as target blendshapes to determine motion of a mouth part and fused with other data.)
Regarding claims 12, 4, 20,
Mann teaches:
The device of claim 9, wherein determining the second to-be-fused data corresponding to the target part comprises: determining mesh point data of a plurality of meshes corresponding to the target part based on the target part data; and determining the mesh point data as the second to-be-fused data. (Previous Mann Col. 24 Line 19, Col. 23 Line 43, Col. 12 Line 44 “The deformable object model may comprise a mesh model formed of polygons such as triangles and/or quadrilaterals each having respective edges and vertices. The adjustable parameters of the deformable object model may…” Col. 13 Line 38 “…the deformation parameters may control weightings for a linear combination of a predetermined set of principal components referred to as blendshapes, where each blendshape corresponds to a specific global deformation of the base geometry” Note: Mann teaches that a shared target area of blendshapes can be identified and fused in a first and second “to-be-fused”. According to this application’s specifications in ¶54 “The first to-be-fused data may be composed of 52 blendshape (single mesh point) data.” ¶63 “the first set of data, that is the first to-be-fused data, is mouth-shape blendshape data, and the other set of data, that is, the second to-be-fused data, is expression blendshape data. For each blendshape data, a maximum value is selected as the target fusion data corresponding to the corresponding blendshape data.” Here the claims first and second to-be-fused data is defined as blendshape data, which in the previous quote is used interchangeably with mesh point data. They are used interchangeably as blendshape data simply refers to mesh point data that is intended to be deformed and has a set of weights defining how it should be deformed. Mann teaches this as well; deformable objects are defined being a mesh with specific point/vertex data. This mesh point data is taught to also have a set of parameters associated that include weights defining how the mesh should be deformed, the mesh data alongside the weight deformation parameters are blendshapes.)
Regarding claims 16, 8,
Mann teaches:
The device of claim 9, wherein the acts further comprise: updating a facial expression and a mouth shape of the target virtual object based on the target fusion data, so that the facial expression and the mouth shape of the target virtual object correspond to those of the target object. (Col. 12 Line 51, cited previously Note: Mann teaches that blendshapes which will be used to make the virtual object of the face specifically include things such as mouth shape and facial expressions.)
Claims 13, 5, are rejected under 35 U.S.C. 103 as being unpatentable over Mann (US 11830159 B1) in view of Bhat (US 10198845 B1) and further in view of Zikky (Semi Automatic Retargeting for Facial Expressions of 3D Characters with Fuzzy logicBased on Blendshape Interpolation).
Regarding claim 13, 5,
Mann teaches:
The device of claim 9, wherein determining the target fusion data based on the first to-be-fused data and the second to-be-fused data comprises: determining a value among the first to-be-fused data and the second to-be-fused data corresponding to a same mesh point, and determining the value as a target mesh point data of the corresponding mesh point; and determining the target fusion data according to the target mesh point data of at least part of mesh points. (Mann Col. 24 Line 2 “Subtracting the primary blendshape from the base blendshape may have the effect of removing or reducing the mouth deformation from the actor's original performance, whilst retaining other aspects of the actor's expression and performance … The resulting sequence of combined blendshapes 324 may therefore represent the actor of the input video 318 exhibiting mouth movements corresponding to the sequence of visemes of the secondary dialogue, whilst retaining expressiveness and other aspects of the performance from the input video 318. Although in the present example the performance transfer 322 involves determining a linear combination of blendshapes, in other examples alternative or additional processing may be performed.” Note: We have established the applications mesh points are analogous to blendshapes. Mann teaches that blendshapes of the same target area, the mouth, are fused from the primary blendshapes and the base blendshapes.)
Mann teaches that fusion of two pieces of mesh data that describe different data for the same mesh points/blendshapes can be fused together. Mann does not however teach the use of a “maximum value” to blend the data which describes the same target area, the identification of maximum values for meshe points/blendshapes which make up a target object is described in Zikky which teaches determining a maximum value among the first to-be-fused data and the second to-be-fused data corresponding to a same mesh point, and determining the maximum value as a target mesh point data of the corresponding mesh point; and determining the target fusion data according to the target mesh point data of at least part of mesh points. (Zikky 2.4 Blendshape on 3D Character Faces “Blendshape technique for an animator is very popular because this method can be done with a simple linear interpolation motion that can interpret facial expressions of a 3D character. The general illustration in this discussion is the formation of facial expressions of the target 3D character which is formed from the weight of basic expressions models such as happy, sad, anger, fear, disgust, and surprise. All expressions are designed and prepared by weighting constraints ( constraints ) between the range of 0 to 1, 0 means being neutral conditions or without weights, while 1 is a condition where the weight given to the maximum value. Animation on the target of the default models is made in the form of neutral, while the weighting expression animation is formed in the state of maximal expression. So that with the shape interpolation weighted through each of the basic expressions, it is expected to form a certain expression as planned. Suppose that if angry expression is desired, it can be given weight by the expression of the mouth open 0.5 and 0.7 for forehead wrinkles and so on.” Note: Zikky teaches that for two blendshapes for a face in different expressions, meaning they share corresponding points, a maximum value for a given point can be determined. By maximum value Zikky refers to the weight given to a point that determines how much the fused blendshape will influence the original first blendshape. Example points of a mouth and forehead are given where a weight can be chosen, potentially the “maximum value” of 1, to determine how much an angry expression blend shape will influence those points.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to modify Mann with Zikki where the fusion of mesh points for two pieces of fusion data that describe the same points in different states can be fused together by leveraging a maximum value where points are weighted allowing one to choose which piece of data influences the point the most.
There are several benefits that would motivate one to do so, as Mann fuses the two pieces of data to maintain a more accurate virtual object of the face that uses the new fused data but maintains the original data’s expressions a higher level of accuracy could be obtained through the maximum value, which would allow one to determine exactly how much each piece of fused data influences the combined result.
Conclusion
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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