DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the Amendment filed on 11/05/2025.
Claims 1-12, 14-20 are pending. Claim 2 has been amended. Claim 13 has been cancelled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2, (3, 17). 4 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5 of app 17/287,254 (now is US patent US 11893673 B2). Although the claims at issue are not identical, they are not patentably distinct from each other because they both claim the same subject matters and limitations as explained below.
Claim 2 is determined to be obvious in light of claim 1 of 17/287,254 (now is US patent US 11893673 B2) based on reasons below for having similar limitations.
Instant application claims 2
17/287,254 claim 1
2. A method for generating a weighted interpolation between two or more Morph Target Shapes relative to a Base Shape, the Morph Target Shapes each including a plurality of topologically consistent vertex coordinates, including the steps of: receiving a plurality of Input Constraint Shapes including a plurality vertex coordinates topologically consistent with those of the Morph Target Shapes, each Input Constraint Shape associated with non-zero weights on one or more of the Morph Target Shapes; generating Additional Constraint Shapes for a plurality of new weightings on Morph Target Shapes using the Input Constraint Shapes, and associating the Additional Constraint Shapes with their respective new weightings; receiving interpolation weightings for each of the two or more Morph Target Shapes; generating an Interpolation Function for interpolating between the two or more Morph Target Shapes using the Base Shape, Input Constraint Shapes and additional Constraint Shapes; using interpolation weightings as arguments to the Interpolation Function to generate vertex coordinates corresponding to the weighted interpolation between the two or more Morph Target Shapes.
1. A method for generating a weighted interpolation between a plurality n of morph target shapes B.sub.1 . . . B.sub.n elative to a base shape B.sub.0 including the steps of: receiving a set of weights W, including for each morph target shape B.sub.k of the morph target shapes B.sub.1 . . . B.sub.n, a weight w.sub.k to be applied to that morph target shape B.sub.k; receiving a plurality m of constraint shapes C.sub.1 . . . C.sub.m, each constraint shape associated with non-zero weights (associated weights) on one or more of the morph target shapes B.sub.1 . . . B.sub.n (associated shapes); generating a continuous multivariate interpolation function configured to reproduce each morph target shape and each constraint shape when a respective morph target shape or a constraint shape's associated weights on associated shapes are provided as arguments to the interpolation function; and using the weights W to be applied to morph target shapes as arguments of the interpolation function to generate the weighted interpolation, wherein the interpolation function has the form:
Claim 3, 17 is determined to be obvious in light of claim 3 of 17/287,254 (now is US patent US 11893673 B2) based on reasons below for having similar limitations.
Instant application claims 3, 17
17/287,254 claim 3
3. The method of claim 2 wherein at least one Input Constraint Shape is a Combination Shape corresponding to a combination between the two or more Morph Target Shapes with unitary weights.
4. The method of claim 3 wherein constraint shapes include combination shapes corresponding to a combination between the two or more morph target shapes with unitary weights
17. The method of claim 12 wherein the mapping of the Control Shapes into at least some of their constituent Component Shapes and associated weightings on each of the constituent Component Shapes is predefined.
and wherein the modifiers applied to combination shapes are a function of the associated weights for each combination shape.
Claim 4 is determined to be obvious in light of claim 5 of 17/287,254 (now is US patent US 11893673 B2) based on reasons below for having similar limitations.
Instant application claims 4
17/287,254 claim 5
4. The method of claim 2 wherein at least one Input Constraint Shape is an Incremental Shape corresponding to a partial weighting of one or more of the Morph Target Shapes.
5. The method of claim 3 wherein constraint shapes include at least one incremental shape corresponding to a partial weighting of one or more of the morph target shapes.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. (FP 7.30.05)This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: Input means to… in Claim 1 of Page 27, Line 3, means for generating… in Claim 1 of Page 27, Line 12,
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. (FP 7.30.06).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 18, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wampler (US 20180130256 A1), in view of Ma et al. (US 20180033190 A1, hereinafter Ma).
Regarding Claim 1, Wampler teaches an animation system (Wampler, Paragraph [0013], disclosed systems and methods generate real-time digital animations) for generating a weighted interpolation between two or more Morph Target Shapes, the animation system comprising (Wampler, Paragraph [0052], the stylized mesh deformation system determines the combined shape-space, deformation interpolation measure based on a weighted (i.e., interpolated) measure of deformation of each of the input meshes): input means to receive a set of input weightings on Morph Target Shapes for the weighted interpolation to be generated (Wampler, Paragraph [0021], a representation of generating a modified mesh based on a combined shape-space, deformation interpolation measure applied to a plurality of input meshes; [0236], determining the ARAP shape-space deformation measure based on a second as-rigid-as-possible-deformation measure of a blended mesh generated by blending the plurality of input meshes according to the weights), a multi-variate continuous Interpolation Function (Wampler, Paragraph [0137], [0151], [0154], deformation interpolation measure by generalizing equation 7 to interpolate between multiple input shapes…the stylized mesh deformation system can continue to perform the acts… This process is repeated until convergence), wherein given a set of shapes including Morph Target Shapes and Constraint Shapes representing weighted combinations of Morph Target Shapes (Wampler, Paragraph [0041], an amount of deformation of a blended mesh generated in a shape space defined by the input meshes to satisfy input constraints (i.e., a “shape-space deformation measure”). The stylized mesh deformation system can then optimize the combined shape-space, deformation interpolation measure to determine weights for combining the different input meshes to generate a modified mesh), the Interpolation Function is configured to reproduce Morph Target Shapes and Constraint Shapes when corresponding weights are provided as input weightings on Morph Target Shapes to the multi-variate continuous function (Wampler, Paragraph [0052], the stylized mesh deformation system determines the combined shape-space, deformation interpolation measure based on a weighted (i.e., interpolated) measure of deformation of each of the input meshes); and means for generating the weighted interpolation (Wampler, Paragraph [0110], determines an edge specific as-rigid-as-possible-deformation measure for each shape, and then combines the edge specific as-rigid-as-possible-deformation measure for each shape based on the weights 312-316 to generate the ARAP input mesh deformation interpolation measure 322) [[ by using the input weightings as arguments to the multi-variate continuous Interpolation Function ]].
But Wampler does not explicitly disclose the Interpolation Function is configured to reproduce Morph Target Shapes and Constraint Shapes when corresponding weights … [[ means for generating the weighted interpolation ]] by using the input weightings as arguments to the multi-variate continuous interpolation function.
However, Ma teaches the Interpolation Function (Ma, Fig. 1, Element Iterative Refinement) is configured to reproduce Morph Target Shapes (Ma, Fig. 1, Element 101 Performance Measurements) and Constraint Shapes (Ma, Fig. 1, Element 102 Template Blendshapes) when corresponding weights (Ma, Fig. 10, Step 1002, Apply Weights/Coefficients to Blendshapes to Best Fit with 3d Stereo Reconstruction) … means for generating the weighted interpolation by using the input weightings as arguments to the multi-variate continuous Interpolation Function (Ma, Fig. 3, Step 304, Adjust Variables to Promote Sparsity in the Computed Blendshape Weights; Paragraph [0075], the neutral pose b0 , and blendshape weights w,) and estimated rigid motion (rotation R, and translation t,) at the ith frame, and p, is the input tracked facial performance; [0037], the first set of weighted blendshapes is modified during the temporal smoothing process to yield the second set of weighted blendshapes; repeating the optimization process for a predetermined number of iterations to yield a final set of weighted blendshapes; [0125], a blendshape is associated with the 3d dense stereo reconstruction, also referred to as a 3d mesh, by interpolating input blendshapes linearly and the closest point on the input mesh for each vertex in the interpolated blendshape).
Ma and Wampler are analogous since both of them are dealing with address generating interpolated/deformed 3D shapes/meshes from multiple shapes using weightings. Wampler provides a multi-shape, weight-driven interpolation framework; Ma provides an explicit coefficient-based blendshape interpolation/weight usage framework. (including endpoint reproduction behavior) for generating an output mesh from input
shapes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate coefficient/weight handling (and associated blending/interpolation implementation) taught by Ma into modified invention of Wampler such that system will be able to achieve predictable, controllable weight-driven interpolation behavior and shape reproduction at corresponding weights, with a reasonable expectation of success because both use weights/coefficients to generate interpolated mesh shapes
Regarding Claim 2, Wampler teaches a method for generating a weighted interpolation between two or more Morph Target Shapes relative to a Base Shape (Wampler, Paragraph [0013], disclosed systems and methods generate real-time digital animations [0021], a representation of generating a modified mesh based on a combined shape-space, deformation interpolation measure applied to a plurality of input meshes), the Morph Target Shapes each including a plurality of [[ topologically consistent ]] vertex coordinates (Wampler, Paragraph [0264], a third as-rigid-as-possible-deformation measure of a first blended mesh generated by blending the first subset of the vertices from the plurality of input shapes according to the first weights), including the steps of: receiving a plurality of Input Constraint Shape including a plurality vertex coordinates [[ topologically consistent ]] with those of the Morph Target Shapes (Wampler, Fig. 1, Step 1110 Receiving input to manipulate a digital model, Paragraph [0021], a representation of generating a modified mesh based on a combined shape-space, deformation interpolation measure applied to a plurality of input meshes; [0053], a first combination of the input shapes 102d and 102e to generate the tail portion 116c of the modified mesh 114 (e.g., combines vertices from the tail portion 116a from the input shape 102d and vertices from the tail portion 116b from the input shape), each Input Constraint Shape associated with [[ non-zero ]] weights on one or more of the Morph Target Shapes (Wampler, Paragraph [0056], deformation interpolation measure to determine weights to apply to any given input shape in generating a modified mesh); generating Additional Constraint Shapes for a plurality of new weightings on Morph Target Shapes using the Input Constraint Shape (Wampler, Paragraph [0053], tail portion 116b from the input shape 102e based on a first set of weights to generate the tail portion 116c). Similarly, the stylized mesh deformation system utilizes a second combination of the input shapes 102a and 102c to generate the neck portion 118c of the modified mesh 114 (e.g., combines vertices from the neck portion 118a of the input shape 102a and the neck portion 118b of the input shape 102c based on a second set of weights to generate the neck portion 118c),
and associating the Additional Constraint Shapes with their respective new weightings
(Wampler, Paragraph [0069], the products of each measure of deformation and the associated weights corresponding to each input shape) to determine the input mesh deformation interpolation measure 228. [0053], the input shape based on a second set of weights <read on new weightings> to generate the neck portion); receiving interpolation weightings for each of the two or more Morph Target Shapes (Wampler, Paragraph [0052], the stylized mesh deformation determines the weights for combining the input meshes 102a-102e by utilizing a combined shape-space, deformation interpolation measure; [0132], its interpolation is slightly different since it combines both different handles and different shapes);
generating an Interpolation Function (Wampler, Paragraph [0137], [0151], [0154], deformation interpolation measure by generalizing equation 7 to interpolate between multiple input shapes…the stylized mesh deformation system can continue to perform the acts… This process is repeated until convergence)
for interpolating between the two or more Morph Target Shapes (Wampler, Paragraph [0135], possible-deformation measure to allow interpolation between a set of shapes) using the Base Shape (Wampler, Paragraph [160], equation 8 employs a local rotation matrix Rgs for each pair of edge group and Base Shape, rather than simply a rotation matrix for each edge group), Input Constraint Shape (Wampler, Paragraph [0089], one function of the elastic energy E is to interpolate between the input shapes as the user-specified constraint set C changes) and additional Constraint Shapes (Wampler, Paragraph [0010], a variety of input parameters (e.g., for modeling the physical characteristics of the physical simulation), physical dynamic equations, and constraints). [[ using interpolation weightings as arguments to the Interpolation Function to generate vertex coordinates corresponding to the weighted interpolation between the two or more Morph Target Shapes.]]
But Wampler does not explicitly disclose [[ the Morph Target Shapes each including a plurality of ]] topologically consistent [[ vertex coordinates, including the steps of: receiving a plurality of Input Constraint Shape including a plurality vertex coordinates ]] topologically consistent [[ with those of the Morph Target Shapes
each Input Constraint Shape associated with ]] non-zero [[ weights on one or more of the Morph Target Shapes ]]; [[ for interpolating between the two or more Morph Target Shapes using the Base Shape, Input Constraint Shape and ]] additional Constraint Shapes; using interpolation weightings as arguments to the Interpolation Function to generate vertex coordinates corresponding to the weighted interpolation between the two or more Morph Target Shapes.
However, Ma teaches topologically consistent with target shapes (Ma, Paragraph [0010], “Significant production time is spent decomposing retopologized face shapes into localized, meaningful poses”); each Input Constraint Shape associated with non-zero weights on one or more of the Morph Target Shapes (Ma, Paragraph [0072], “The barycentric equation matrix B is built using one row per world space match with only three non-zero entries for (α,β,γ)”); for interpolating between the two or more Morph Target Shapes using the Base Shape, Input Constraint Shape and additional Constraint Shapes (Ma, Paragraph [0093], “By optimizing in this way, the region that each new shape is allowed to vary is limited and the deformation semantics of each expression are well maintained”); using interpolation weightings as arguments to the Interpolation Function to generate vertex coordinates (Ma, Paragraph [0015],”each template blendshape is defined by data representative of a plurality of vertices and relationships between said vertices”) corresponding to the weighted interpolation between the two or more Morph Target Shapes (Ma, Paragraph [0125], “the coefficients of the applicable blendshapes for a given frame, which can be a neutral shape (a coefficient of 1 for neutral and 0 for other shapes) or imported from prior calculations… a blendshape is associated with the 3d dense stereo reconstruction, also referred to as a 3d mesh, by interpolating input blendshapes linearly and the closest point on the input mesh for each vertex in the interpolated blendshape. In the iterative coefficient update process… the iterations converge on a set of coefficients, the process stops and a plurality of optimized coefficients or weights for a plurality of blendshapes in each frame is generated as the output”).
Ma and Wampler are analogous references because both are directed to generating interpolated or deformed 3D shapes from a plurality of input shapes using weightings in a 3D modeling/animation environment. Wampler provided a weight-driven, constraint based shape interpolation framework for generating modified meshes from multiple input shapes. Ma provided a well-known blendshape interpolation framework in which coefficients (weights) are explicitly applied to input blendshapes to generate interpolated meshes, and further teaches iteratively determining and applying such coefficients to produce predictable interpolated vertex coordinates. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate weight-driven interpolation system taught by
Wampler into modified invention of Ma which uses coefficient-based blendshape
interpolation technique such that a combination applies a known weight-based blending
technique to a similar weight-driven multi-shape interpolation framework in order to
achieve predictable generation of interpolated meshes for different input weightings,
with a reasonable expectation of success, because both references operate on the
same principle of generating output shapes as weighted combinations of input shapes
in 3D modeling systems.
Regarding Claim 3, the combination of Wampler and Ma teaches the invention in Claim 2.
The combination further teaches wherein at least one Input Constraint Shape is a Combination Shape corresponding to a combination between the two or more Morph Target Shapes with unitary weights (Wampler, Paragraph [0053], combination of the input shapes 102d and 102e to generate the tail portion 116c of the modified mesh 114 (e.g., combines vertices from the tail portion 116a from the input shape 102d and vertices from the tail portion 116b from the input shape 102e based on a first set of weights to generate the tail portion 116c). Similarly, the stylized mesh deformation system utilizes a second combination of the input shapes 102a and 102c to generate the neck portion 118c of the modified mesh); [0058], input mesh deformation interpolation measure” refers to a weighted quantification of deformation of all or a portion of vertices in a plurality input meshes).
Regarding Claim 4, the combination of Wampler and Ma teaches the invention in Claim 2.
The combination further teaches wherein at least one Input Constraint Shape is an Incremental Shape (Wampler, Paragraph [0062], the stylized mesh deformation system generates modified meshes that smoothly and gradually transition between the input meshes 202-206. In particular, the stylized mesh deformation system gradually combines the input meshes 202-206 to generate the modified meshes [0082], the stylized mesh deformation system can operate in conjunction with nonphysical shapes or simulated physical shapes) corresponding to a partial weighting of one or more of the Morph Target Shapes (Wampler, Paragraph [0062], the stylized mesh deformation system generates modified meshes that smoothly and gradually transition between the input meshes. [0057], a portion of a mesh generated by a blending algorithm that blends input shapes within a shape space according to one or more weights. [0070], the stylized mesh deformation system defines the weights 220-224 such that they must be positive and sum to one (or some other set value)).
Regarding Claim 18, the combination of Wampler and Ma teaches the invention in Claim 2.
The combination further teaches wherein the generated weighted interpolation is to be visualized on an end user display device of an electronic computing device (Wampler, Paragraph [0019], FIGS. 1A-1C illustrate a representation of manipulating a digital model via a computing device based on a plurality of input meshes in accordance with one or more embodiments; [0041], deformation interpolation measure to determine weights for combining the different input meshes to generate a modified mesh. Utilizing the combined shape-space, deformation interpolation measure, the stylized mesh deformation system can smoothly combine input meshes in response to changing user input, [0047], the stylized mesh deformation system provides an initial mesh for display with control points to allow a user to provide user input for generating a modified mesh. In particular, FIG. 1B illustrates the computing device 104 displaying).
Regarding Claim 19, the combination of Haaland and Keenan teaches the invention in Claim 12.
The combination further teaches s wherein each of the steps is executed on an electronic computing device (Wampler, Paragraph [0194], hen executed by the one or more processors, the computer-executable instructions of the stylized mesh deformation system 900 can cause a client device and/or a server device to perform the methods described herein).
Regarding Claim 20, the combination of Wampler and Ma teaches the invention in Claim 18.
The combination further teaches wherein the generated weighted interpolation is displayed (Wampler, Paragraph [0202], deformation interpolation measure and determine weights for combining the selected input meshes (e.g., via the deformation optimization facility 908). Furthermore, the server(s) 1006 can generate a modified mesh by combining the selected input meshes based on the ARAP combined shape-space, deformation interpolation measure (e.g., via the mesh generator 910). Moreover, the server(s) 1006 can provide the modified mesh for display to the client device).
Claim(s) 5-6, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wampler (US 20180130256 A1), in view of Ma et al. (US 20180033190 A1, hereinafter Ma) as applied to Claim 1 above and further in view of Chen et al. (US 20190139277 A1, hereinafter Chen).
Regarding Claim 5, the combination of Wampler and Ma teaches the invention in Claim 2.
The combination does not explicitly disclose but Chen teaches wherein the additional Constraint Shapes are generated at new weightings such as to complete or augment an n-dimensional cube (Chen, Paragraph [0041], As used herein, the term “cell” refers to a three-dimensional digital shape (e.g., a cube) that forms a portion of a digital cage; [0159], determines and employs one or more deformation weights to modify bristle vertices to new spatial locations within a deformed digital cage) wherein Morph Target Shapes are dimensions of the n-dimensional cube having the Base Shape as the origin and edges of the n-dimensional cube as weightings on Morph Target Shapes (Chen, Paragraph [0041], As used herein, the term "cell" refers to a three-dimensional digital shape ( e.g., a cube) that forms a portion of a digital cage… a cell includes a three-dimensional cube that encompasses a subset of bristle vertices from a subset of bristles in a digital brush. Indeed, each portion of a bristle that passes through a cell can include one or more bristle vertices located within the cell. In addition, each cell can include cell vertices, such as corners of the cell where three or more edges meet).
Chen and Wampler are analogous since both of them are dealing with of shapes deformation in 3D modeling. Wampler provided a way of using multiple shapes deformation using continuously weighted shape blending in the 3D modelling.
Chen provided a way of using spatial location of a cube object vertex change to updating the weight for deformation during the 3D modelling. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate interpolation of weight changes taught by Chen into modified invention of Wampler such that during the 3D modelling, system will be able to generalize cube/weight deformation framework to higher dimensional weight
spaces when interpolating among multiple morph target dimensions. The motivation is to allow underlying operation to apply independent weights along independent degrees of freedom to generate new constrained shapes with more realistic result.
Regarding Claim 6, the combination of Wampler, Ma and Chen teaches the invention in Claim 5.
The combination further teaches wherein additional Constraint Shapes are generated using a meshless interpolation method (Chen, Paragraph [0072], the brush deformation system models deformation of digital cages utilizing the approach for deforming three-dimensional objects described by Matthias Müller, Bruno Heidelberger, Matthias Teschner, and Markus Gruss in Meshless Deformations Based on Shape Matching).
Chen and Wampler are analogous since both of them are dealing with of shapes deformation in 3D modeling. Wampler provided a way of using multiple shapes deformation using continuously weighted shape blending in the 3D modelling. Chen provided a way of using spatial location of a cube object vertex change to updating the weight for deformation during the 3D modelling using Meshless deformation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate generation of additional constraint shapes using the meshless deformation approach taught by Chen into modified invention of Wampler such that during the 3D modelling, system will be able to model deformation using a meshless deformation approach (Meshless Deformations Based on Shape Matching) in order to track weight changing of edges of cubed object in to precisely updating the modeling result using meshless deformation which increase the flexibility of the modelling system.
Regarding Claim 8, the combination of Wampler and Ma teaches the invention in Claim 2.
The combination further teaches including the step of partitioning the n-dimensional cube into lower dimensional spaces and generating additional Constraint Shapes for each lower dimensional space (Wampler, Paragraph [0083], the shape of the sth input mesh, denoted by p.sub.s, is represented with a one-dimensional array of length |custom-character|, each element of which is a k-dimensional vector describing the position of a single vertex, where k is either two or three depending on the dimension of the space the mesh).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wampler (US 20180130256 A1), in view of Ma et al. (US 20180033190 A1, hereinafter Ma), further in view of Chen et al. (US 20190139277 A1, hereinafter Chen) as applied to Claim 6 above and further in view of Su et al. (US 20140064588 A1, hereinafter Su)
Regarding Claim 7, the combination of Wampler, Ma and Chen teaches the invention in Claim 6.
The combination further teaches wherein additional Constraint Shapes are generated using [[ radial basis ]] interpolation (Wampler, Paragraph [0034], an “input mesh deformation interpolation measure”) and (2) an amount of deformation of a blended mesh generated in a shape space defined by the input meshes to satisfy input constraints) .
The combination does not explicitly disclose the generation is using radial basis interpolation.
However, Su teaches shapes are generated using radial basis interpolation (Su, Paragraph [0009], the step of forming the morphed meshes uses one of the input meshes as a generic mesh, and for each of the other input meshes forms the corresponding morphed mesh by deforming the generic mesh to the shape of the other input mesh. The deformation of the generic mesh can be performed using a radial basis function (RBF) morphing).
Su and Wampler are analogous since both of them are dealing with of shapes deformation in 3D modeling. Wampler provided a way of using multiple shapes deformation using continuously weighted shape blending in the 3D modelling. Su provided a way of using radial basis interpolation during the 3D modelling. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate RBF morphing as a known interpolation technique for generating intermediate/deformed meshes from input meshes taught by Su into modified invention of Wampler such that during the 3D modelling, system will be able to using a radial basis function (RBF) approach with progressive projection coupled with local smoothing in generation of additional constraint shapes in the combined system to crate predictable results with a reasonable expectation of success to ensure robustness.
Claim(s) 9-12, 14, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haaland (US 6415233 B1), in view of Keenan (US 7840626 B1).
Regarding Claim 9, Haaland teaches a method for estimating underlying Component Shape weights of a Complex Shape including the steps of (Haaland, Column 1, Line 21-25, “This invention relates to classical least squares (CLS) multivariate spectral analysis methods. More particularly, this invention relates to an improvement to the prediction phase of the CLS method wherein the spectral shapes of constituents” Column 2, Line 3-5, “a pxm matrix of the m pure-component spectra of all spectrally active components”): receiving one or more suggested Component Shapes (Haaland, Column 8, Line 11-14, “If the component shape is unaffected by matrix effects, the spectrum of the pure component can be obtained by simply measuring the spectrum of the pure component”); and obtaining Component Shape weights through solving least square problem (Haaland, Column 4, Line 21-35, Column 11, Line 44-46, “Examples of added features for CLS are weighted least squares, generalized least squares, base-line fitting” Column 13, Line 37-41, “linear slope, and quadratic curvature (i.e., a quadratic baseline) were simultaneously fit along with the CLS-estimated pure component spectra of the four molecular components and drift spectral component shape”)
Haaland does not explicitly disclose but Keenan teaches where penalties and Solution Boundaries are enforced to ensure the weights associated with suggested Component Shapes are nonzero (Keenan, Column 3, Line 1-5, “the rotated loading matrix and the rotated scores matrix can be further refined using constrained least squares, constrained alternating least squares, or nonnegative matrix factorization approaches” Column 14, Line 16-20, 31-37, “Component refinement is accomplished by forcing the spectral and abundance components to satisfy physically motivated constraints. For example, physically admissible concentrations and spectra must typically be non-negative” “Step 63 shows a constrained-least-squares approach to refine either the unweighted rotated loading matrix (i.e., at step 75), or the weighted rotated loading matrix (i.e., at step 80), using the data matrix D. The refined loading matrix PP can be computed from the data and the feasible estimate for the scores matrix TP by least squares, subject to constraints”).
Keenan and Haaland are analogous since both of them are dealing with spatial process of image analysis. Haaland provided a way of process using calibrated data set to express the spectral shapes and measure pure sample constituents. Keenan provided a way of using application of non-negativity constraints and enforce of physically motivated constraints and use the least squares in process the spatial shapes during the process of image analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate weighted matrix calculation taught by Keenan into modified invention off Haaland such that during the image analysis, system will be able to precisely calculate and adjust the shape in order to create best spectral shape and reduce the errors to minimal.
Regarding Claim 10, the combination of Haaland and Keenan teaches the invention in Claim 9.
The combination further teaches wherein the method includes the step of receiving weights for at least one but not all of the suggested Component Shapes and using the received weights to restrict the solution to the least square problem (Haaland, Column 11, Line 43-45, “All methods previously developed for CLS should also work for PACLS. Examples of added features for CLS are weighted least squares, generalized least squares, base-line fitting” Column 14, Line 1-5, This improvement can be at least partially realized by making use of a subset of sample spectra obtained. on the spectrometer during the variable temperature experiment. Column 4, Line 21-35, Column 11, Line 44-46, “Examples of added features for CLS are weighted least squares, generalized least squares, base-line fitting”; it is noted squares variants (including weighted least squares) use of subset/reduced spaces in estimation, which restrict the least squares solution using partial weights and operating in reduced dimensional spaces).
Regarding Claim 11, the combination of Haaland and Keenan teaches the invention in Claim 9.
The combination further teaches wherein underlying Component Shape weights are estimated on a lower dimensional space (Haaland, Column 2, Line 54-57, “first utilizing a previously constructed calibration data set expressed as matrix K representing the combination of vectors expressing the spectral shapes and concentrations” Column 4, Line 9-12, “using sample spectra obtained on the primary spectrometer plus spectral difference shapes from 5 subset samples run on both spectrometers. The line of identity and the linear least-squares fit to the data are indistinguishable in this plot” Column 14, Line 1-5, “This improvement can be at least partially realized by making use of a subset of sample spectra obtained. on the spectrometer during the variable temperature experiment”; it is noted subset of sample spectra will projected into reduced component space).
Regarding Claim 12, Wampler teaches a method for generating a weighted interpolation between two or more Control Shapes, at least one of the Control Shapes being a Complex Shape comprising a weighted combination of a plurality of Component Shape, including the steps of (Haaland, Column 11, Line 43-45, Line 52-55, “All methods previously developed for CLS should also work for PACLS. Examples of added features for CLS are weighted least squares”…Application of New Least Squares Methods for the Quantitative Infrared Analysis of Multicomponent Samples, "It should be noted that any spectral preprocessing should be applied to the original calibration spectra, the prediction spectra, and the spectral shapes added in the PACLS method”): mapping the Control Shapes into their constituent Component Shapes and associated weightings on each of the constituent Component Shapes to `target Component Shapes (Haaland, Column 11, Line 35-55, the component spectral shape was not present in the calibration spectra but is present in the unknown sample spectrum <read on control shape> to be predicted, then the PACLS-estimated component concentration of the added shape should be a good estimate…examples of added features for CLS are weighted least squares, generalized least squares, base-line fitting, pathlength correction, multi-band analysis…. It should be noted that any spectral preprocessing should be applied to the original calibration spectra, the prediction spectra, and the spectral shapes <read on component shape> added in the PACLS method); providing the set of weighted target Component Shapes to an Interpolator (Haaland, Column 2, Line 51-55, 65-67, “A method for performing an improved classical least squares multivariate estimation of the quantity of at least one constituent of a sample comprising first utilizing a previously constructed calibration data set expressed as matrix…“one additional constituent or additional system effect not present in the calibration data set but present in the prediction data set to form an augmented matrix {tilde over (K)}, and estimating the quantity of at least one of the constituents in the calibration data set that is present in the sample by utilizing the augmented matrix”);
Haaland does not explicitly disclose but Keenan teaches using the Interpolator to interpolate between the set of weighted target Component Shapes to generate the weighted interpolation between the two or more Control Shapes (Keenan, Column 3, Line 1-5, “the rotated loading matrix and the rotated scores matrix can be further refined using constrained least squares, constrained alternating least squares, or nonnegative matrix factorization approaches”; it is noted the refinement will estimate and stify motivated constraints so the weights are non-zero which is interpolating between weighted component shapes).
Keenan and Haaland are analogous since both of them are dealing with spatial process of image analysis. Haaland provided a way of process using calibrated data set to express the spectral shapes and measure pure sample constituents. Keenan provided a way of using refining component estimate using constrained optimization which control shape and component shapes for spatial process of image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate interpolation of shape process taught by Keenan into modified invention off Haaland such that during the image analysis, system will be able to dynamically adjust the shape in order to create best spectral shape result during the image analysis.
Regarding Claim 14, the combination of Haaland and Keenan teaches the invention in Claim 12.
The combination further teaches wherein the step of mapping the one or more Complex Shapes into their constituent Component Shapes includes estimating underlying Component Shape weights of a Complex Shape including the steps of (Haaland, Column 1, Line 21-25, “This invention relates to classical least squares (CLS) multivariate spectral analysis methods. More particularly, this invention relates to an improvement to the prediction phase of the CLS method wherein the spectral shapes of constituents” Column 2, Line 3-5, “a pxm matrix of the m pure-component spectra of all spectrally active components”): receiving one or more suggested Component Shapes (Haaland, Column 8, Line 11-14, “If the component shape is unaffected by matrix effects, the spectrum of the pure component can be obtained by simply measuring the spectrum of the pure component”); and obtaining Component Shape weights through solving least square problem (Haaland, Column 4, Line 21-35, Column 11, Line 44-46, “Examples of added features for CLS are weighted least squares, generalized least squares, base-line fitting” Column 13, Line 37-41, “linear slope, and quadratic curvature (i.e., a quadratic baseline) were simultaneously fit along with the CLS-estimated pure component spectra of the four molecular components and drift spectral component shape”)
Haaland does not explicitly disclose but Keenan teaches where penalties and Solution Boundaries are enforced to ensure the weights associated with suggested Component Shapes are nonzero (Keenan, Column 3, Line 1-5, “the rotated loading matrix and the rotated scores matrix can be further refined using constrained least squares, constrained alternating least squares, or nonnegative matrix factorization approaches” Column 14, Line 16-20, 31-37, “Component refinement is accomplished by forcing the spectral and abundance components to satisfy physically motivated constraints. For example, physically admissible concentrations and spectra must typically be non-negative” “Step 63 shows a constrained-least-squares approach to refine either the unweighted rotated loading matrix (i.e., at step 75), or the weighted rotated loading matrix (i.e., at step 80), using the data matrix D. The refined loading matrix PP can be computed from the data and the feasible estimate for the scores matrix TP by least squares, subject to constraints”).
Keenan and Haaland are analogous since both of them are dealing with spatial process of image analysis. Haaland provided a way of process using calibrated data set to express the spectral shapes and measure pure sample constituents. Keenan provided a way of using application of non-negativity constraints and enforce of physically motivated constraints and use the least squares in process the spatial shapes during the process of image analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate weighted matrix calculation taught by Keenan into modified invention off Haaland such that during the image analysis, system will be able to precisely calculate and adjust the shape in order to create best spectral shape and reduce the errors to minimal).
Regarding Claim 17, the combination of Haaland and Keenan teaches the invention in Claim 12.
The combination further teaches wherein the mapping of the Control Shapes into at least some of their constituent Component Shapes and associated weightings on each of the constituent Component Shapes is predefined (Haaland, Column 2, Line 51-58, “A method for performing an improved classical least squares multivariate estimation of the quantity of at least one constituent of a sample comprising first utilizing a previously constructed calibration data set expressed as matrix K representing the combination of vectors expressing the spectral shapes and concentrations of the measured pure sample constituents of the calibration data set” Column 1, Line 24-29, method wherein the spectral shapes of constituents or other factors that were not measured in the calibration data set are added to the prediction data set prior to conducting the least squares prediction of at least one of the members of the measured calibration data set found in the prediction data set).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haaland (US 6415233 B1), in view of Keenan (US 7840626 B1) as applied to Claim 9 above and further in view of Wampler (US 20180130256 A1).
Regarding Claim 15, the combination of Haaland and Keenan teaches the invention in Claim 9.
The combination does not explicitly disclose but Wampler teaches wherein Complex Shapes represent one or more of the group consisting of: emotional expressions, visemes and facial expressions unique to an individual (Wampler, Paragraph [0166], Researchers have found facial expression manipulation to be a domain where this applies particularly well. [0053], “the stylized mesh deformation system can generate a variety of expressive, unique modified meshes < read on Complex Shapes > from a small set of input meshes”).
Wampler and Haaland are analogous since both of them are dealing with spatial process of image analysis. Haaland provided a way of process using calibrated data set to express the spectral shapes and measure pure sample constituents. Wampler provided a way of using multiple shapes deformation using continuously weighted shape blending in the 3D modelling by using emotional expression of user. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate different shapes including facial expression taught by Wampler into modified invention off Haaland such that during the image analysis, system will be able to enhance the functionality by using different shape expression including user facial expression which provide variety of shape process data and increase the flexibility of the system.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haaland (US 6415233 B1), in view of Keenan (US 7840626 B1) as applied to Claim 9 above and further in view of Ma et al. (US 20180033190 A1, hereinafter Ma).
Claim 16, the combination of Haaland and Keenan teaches the invention in Claim 9.
The combination does not explicitly disclose but Ma teaches wherein Component Shapes represent FACS action units (Ma, Paragraph [0007], “To span a sufficient array of actor-specific facial expressions which is consistent across human faces, this collection of blend shapes is usually designed to isolate muscle group action units according to the Facial Action Coding System (FACS).”).
Ma and Haaland are analogous since both of them are dealing with spatial process of image analysis. Haaland provided a way of process using calibrated data set to express the spectral shapes and measure pure sample constituents. Ma provided a way of using function with coefficients with blending weights to continuously do the interpolation of when dealing with the deformation of the shape in the 3D modelling according to Facial Action Coding System (FACS). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate different shapes including FACS taught by Ma into modified invention off Haaland such that during the image analysis, system will be able to use FACS to standardized, objective method for describing facial movements and to do detailed understanding of how emotions are expressed through facial changes which provide more precisely image analysis.
Response to Arguments
In response to the applicant’s comment on the rejection of Claims 2, (3, 17). 4 under Nonstatutory Double Patenting, although applicant mentioned in the argument that Terminal Disclaimer has been submitted. However, at the time of this office action mailed, no formal Terminal Disclaimer has been filed on file. Therefore, the original Nonstatutory Double Patenting sustained.
The rejection of Claim 13 under 35 U.S.C. 112(d) or 35 U.S.C. 112 (pre-AIA ), fourth paragraph are withdrawn in view of Applicant’s cancelation of Claim 13.
Applicant’s arguments filed on 03/02/2015, with respect to rejection under 35 USC § 103 has been fully considered but they are not persuasive.
In regard to Claim 1, Applicant argues that Wampler teaches only a deformation
measure, not a multi-variate continuous Interpolation Function. Applicant asserts that Wampler discloses only a "combined shape-space, deformation interpolation measure" and therefore does not teach the claimed "multi-variate continuous Interpolation Function."
In response to the argument, under the broadest reasonable interpretation, the claimed "multi-variate continuous Interpolation Function" encompasses any continuous mapping from a plurality of input weightings (i.e., a multivariate weight vector) to an
interpolated output shape. Wampler explicitly discloses a system in which a set of
weights is applied across multiple input shapes and used to generate a
modified/interpolated mesh through a continuous optimization process that converges
on a solution. The fact that Wampler expresses the interpolation in terms of a
deformation/interpolation "measure" does not remove it from being an interpolation
function, as the measure is the mathematical mechanism by which the interpolated
output is computed. Importantly, Wampler's interpolation operates over multiple input shapes, varies continuously with changes in weights, and yields an output mesh as a function of those weights. Therefore, Wampler teaches a multivariate continuous interpolation function under a broad but reasonable interpretation.
Applicant further argues that Wampler does not reproduce Morph Target Shapes and Constraint Shapes when corresponding weights are provided. Applicant asserts that Wampler fails to teach an interpolation function configured to reproduce Morph Target Shapes and Constraint Shapes when corresponding weights are provided.
In response to the argument, in weight-based shape interpolation systems, reproduction of a particular input shape is achieved by providing the corresponding weight set that selects that shape (e.g. , assigning full weight to one shape and zero weight to others, or using the shape's associated weight vector). Wampler explicitly teaches generating a modified mesh by combining input meshes based on weights, which inherently includes reproducing an input shape when its corresponding weights are applied.
Furthermore, Ma explicitly teaches coefficient-based blendshape interpolation in which
a blendshape output is obtained by linearly blending input blendshapes based on
coefficients, including the explicit case where a neutral shape is represented by a
coefficient of 1 for the neutral shape and 0 for others. This expressly teaches
reproduction of shapes when corresponding weights are provided. And as explained in the rejection above, properly relied on Ma to clarify this reproduction behavior in the combined system.
Applicant further argues that Weights are not used as arguments to the Interpolation
Function Applicant asserts that neither Wampler nor Ma teaches "using the input weightings as arguments to the multi-variate continuous Interpolation Function," asserting that
Wampler merely combines deformation measures "based on weights."
In response to the argument, "Using weights as arguments" simply requires that the
weights serve as inputs to the computation that produces the interpolated output. Ma
explicitly teaches that output blendshapes are obtained by blending input blendshapes
based on coefficients, i.e., the coefficients are the independent variables that
determine the interpolation result. Thus, the coefficients/weights are the arguments to
the interpolation function. Wampler likewise teaches computing an interpolated output mesh by applying weights to multiple input shapes. Accordingly, the combination of Wampler and Ma teaches using input weightings as arguments to the interpolation function.
Applicant further argues that "No articulated motivation to combine Wampler and Ma" Applicant asserts that the Office Action lacks articulated reasoning with rational
underpinning.
In response to the argument, Wampler and Ma are in the same field of endeavor and
both address the problem of generating interpolated 30 shapes from multiple basis
shapes using weights. Ma provides an explicit coefficient-driven blending framework
that yields predictable interpolated outputs and endpoint reproduction behavior. It would
have been obvious to one of ordinary skill in the art to incorporate Ma's coefficient based
blending and weight handling into Wampler's multi-shape interpolation framework
to achieve predictable, controllable interpolation behavior and robust weight-driven
shape generation. Such a combination amounts to applying a known technique to a
similar system to yield predictable results, with a reasonable expectation of success.
Hence the combination of prior arts fully anticipates all the limitations. Therefore,
applicant remark cannot be considered persuasive.
In regard to Claim 2, Applicant argues that "Input Constraint Shapes associated with non-zero weights". Applicant asserts that Wampler does not teach receiving Input Constraint Shapes associated with non-zero weights, asserting that Wampler merely combines vertices.
In response to the argument, Under the broadest reasonable interpretation, "Input
Constraint Shapes" read on shapes or shape components used to constrain or define
the interpolation space. Wampler teaches multiple input shapes and applies non-zero
weights to those shapes (or portions thereof) when generating an interpolated output.
Where a shape contributes to the interpolated result through an assigned weight, that
shape is associated with a non-zero weight as claimed.
Applicant further argues that "Generating Additional Constraint Shapes for new
weightings"; Applicant asserts that Wampler does not teach generating Additional Constraint Shapes for new weightings using the Input Constraint Shapes.
In response to the argument, In a weight-based interpolation system, new weight
vectors necessarily generate new interpolated shape instances derived from the input
shapes. Ma explicitly teaches generating output blendshapes by blending input
blendshapes based on coefficients; changing the coefficients yields new resulting
shapes. Under a broad but reasonable interpretation, these newly generated shapes
correspond to the claimed "Additional Constraint Shapes" generated for new weightings
using the Input Constraint Shapes.
Applicant further argues that "Using interpolation weightings as arguments to generate vertex coordinates"; Applicant asserts that neither reference teaches using interpolation weightings as arguments to generate vertex coordinates.
In response to the argument, Both Wampler and Ma generate output
meshes/blendshapes whose geometry is defined by vertex coordinates. In Ma, blending
input blendshapes based on coefficients necessarily produces vertex coordinates of the
resulting interpolated mesh. Since the coefficients determine the blending result, the
interpolation weightings are used as arguments to generate vertex coordinates
corresponding to the weighted interpolation. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive.
Applicant further assets the rationale in Claim 2 appears to be based on hindsight
reconstruction using applicant's disclosure rather than any teaching or suggestion in the
prior art references themselves.
In response to applicant's argument that the examiner's conclusion of obviousness in Claim 2, with respect to rejection under 35 USC § 103 is based upon improper hindsight reasoning. As a matter of fact, on the contrary, Wampler already teaches generating modified meshes by combining multiple input meshes based on weights (i.e., weight-driven shape blending/interpolation), and Ma already teaches a coefficient/weight-based blendshape framework where applying coefficients/weights to blendshapes yields an interpolated mesh and the coefficients are iteratively optimized and used to generate the resulting mesh. One of ordinary skill in the art, faced with implementing or refining Wampler's weight driven interpolation/constraint-based mesh generation, would have looked to Ma's well-known coefficient/weight-based blendshape interpolation technique as a predictable way to represent and apply weightings (as inputs/arguments) to generate interpolated vertex coordinates and resulting meshes for different weightings, thereby improving controllability/robustness of the interpolation without relying on Applicant's disclosure. It must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In regard to Claim 3 and 4, applicant argues that Claims 3 and 4 depend from claim 2 and fall with claim 2 for at least the same reasons.
With respect to claim 3, weight-based combinations of shapes inherently include
combinations corresponding to unitary weight selections, as taught by Wampler and
further clarified by Ma's coefficient framework.
With respect to claim 4, Wampler explicitly teaches gradual and smooth transitions
between input shapes achieved by varying weights, which inherently produces
intermediate or incremental shapes corresponding to partial weightings.
Hence the combination of prior arts fully anticipates all the limitations. Therefore,
applicant remark cannot be considered persuasive.
In regard to Claim 5, applicant argues that Chen only teaches 3D "cells/cubes," not an "n-dimensional cube" whose dimensions correspond to Morph Target Shapes, with
edges representing weightings.
In response to the argument, Claim 5 does not require that Chen explicitly use the term "n-dimensional cube" or that Chen's "cube" be described in the same animation-specific terminology as Applicant's disclosure. Chen teaches a cube-based structure with
vertices/edges (i.e., a "cell" that is a three-dimensional cube with corners/edges) and
further teaches determining and employing deformation weights to move vertices to new
locations, i.e., generating shapes at new weightings. It would have been obvious to one
of ordinary skill in the art to generalize Chen's cube-based weighted deformation
framework to higher-dimensional parameter spaces when multiple morph targets
(multiple weighting dimensions) are used, because the underlying operation is applying
weights along independent degrees of freedom to generate new constraint shapes.
Therefore, Chen remains evidence of generating additional constraint shapes at new
weightings in a cube/edge/vertex framework. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive.
In regard to Claim 6, 8, applicant argues that Chen is a brush deformation system and not teaching meshless interpolation for additional constraint shapes.
In response to the argument, Claim 6 only requires that additional Constraint Shapes
are generated using a meshless interpolation method, and Chen expressly teaches
modeling deformation of three-dimensional objects using "Mesh less Deformations
Based on Shape Matching." That disclosure is evidence of using meshless
deformation/interpolation techniques to generate deformed shapes (i.e., constraint
shapes) from weighting/deformation operations. Further, Chen is reasonably pertinent
to the problem of generating deformed shapes under weighted constraints in 30
modeling environments, and therefore is analogous. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive.
In regard to Claim 7, applicant argues that The Office Action has failed to establish that Su, which is directed to medical imaging and cardiac mesh analysis, is analogous art to
the animation systems of Wampler and Ma.
In response to the argument, While Wampler/Ma/Chen do not expressly recite "radial
basis interpolation," Su explicitly teaches deforming a generic mesh to the shape of
another mesh using radial basis function morphing. Because the claimed "Additional
Constraint Shapes" are shapes generated for new weightings/constraints, substituting
(or implementing) the interpolation/deformation step using Su's RBF morphing is a
predictable use of a known technique to generate deformed shapes from input shapes.
Therefore, it would have been obvious to implement the generation of additional
constraint shapes using RBF interpolation as taught by Su.
In regard to Claim 9-1 2, 14, and 17,, applicant argues that Haaland and Keenan are
non-analogous because they are directed to spectral analysis rather than animation
systems.
In response to the argument, the recited limitations of claims 9-12, 14, and 17 are
directed to mathematical estimation of component weights using least squares
techniques, including use of constraints/penalties, reduced dimensional spaces, and
use of predefined mapping/calibration frameworks. Such mathematical techniques are
not confined to any single application domain. Haaland teaches classical least squares
and variants such as weighted least squares and related estimation techniques, and
Keenan teaches constrained least squares / constrained refinement methods.
Accordingly, to the extent claim 9 and claim 14 require that the enforced
penalties/solution boundaries guarantee strictly positive (nonzero) weights for the
suggested component shapes, that specific aspect is not explicitly taught by the cited
Keenan passages and would require additional evidence or a revised mapping
consistent with the broadest reasonable interpretation of "nonzero." (For example, if the
claim is reasonably interpreted to require that at least one suggested component shape
has a nonzero weight, such is consistent with constrained solutions that exclude the all zero
solution; however, Applicant's arguments suggest they intend "nonzero" to mean
strictly positive per-component.)
In response to Claims 10-11, Haaland teaches least squares estimation and expressly discusses weighted least squares and related approaches, including use of subsets of data and estimation in reduced spaces, which correspond to receiving weights for at least one but not all suggested components and restricting/solving the least squares
problem, and estimating weights in a lower dimensional space.
In response to Claim 12, mapping a complex/control signal into constituents with
associated weights and providing to an interpolator. under broadest reasonable
interpretation, the claimed "Control Shapes" and "Component Shapes" encompass any
composite representation and its constituent components with associated weights.
Haaland teaches a model-based decomposition/estimation framework using matrices
representing constituent shapes/components and estimating their quantities/weights;
Keenan teaches constrained refinement of component estimates using constrained
least squares methods. Thus, the combination teaches mapping a composite
representation into constituent components with associated weights and providing the
weighted constituents to an estimation/interpolation process.
In regard to Claim 17, Applicant's arguments do not overcome the rejection of claim 17.
In response to the argument, Haaland teaches use of a previously constructed calibration data set/matrix representing constituent components and relationships before prediction/estimation, which corresponds to a predefined mapping framework used during the estimation process. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive.
In regard to Claim 15, applicant argues that Complex Shapes represent emotional
expressions/ visemes / facial expressions unique to an individual.
In response to the argument, Wampler expressly teaches facial expression
manipulation as an application domain and further teaches generating "expressive,
unique modified meshes" from a small set of input meshes, which corresponds to
complex shapes representing facial expressions (including emotional expressions and
visemes) and individual-specific expressions in facial animation systems. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive.
In regard to Claim 16, applicant argues that Component Shapes represent FACS
action units.
In response to the argument, Ma expressly teaches that the collection of blend shapes is designed to isolate muscle group action units according to the Facial Action Coding System (FACS), which corresponds to component shapes representing FACS action units. Hence the combination of prior arts fully anticipates all the limitations. Therefore, applicant remark cannot be considered persuasive
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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THIS ACTION IS MADE FINAL. 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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/YuJang Tswei/Primary Examiner, Art Unit 2614