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 Arguments
Applicant’s arguments, see page 8, filed 03/20/2026, with respect to the abstract have been fully considered and are persuasive. The objection of the abstract has been withdrawn.
Applicant's arguments filed on 03/20/2026 have been fully considered but they are not persuasive. Applicant contends that the amended claims recite “specific, concrete operations that cannot practically be performed in the human mind,” particularly emphasizing the use of an artificial neural network (ANN) including transformer layers and a graph convolution module. The determination of whether a claim recites a judicial exception depends on the claimed steps themselves, not on the recitation of the tools used to perform them. The limitations of claim 1 continue to recite mental processes, as a human can identify/classify the depicted body part, reason about spatial relationships, and conceptualize or sketch a 3D representation either mentally or using pen and paper as an aid. Accordingly, claim 1 recites a judicial exception under Step 2A, Prong One.
Applicant also argues that the specific architecture, particularly “a graph convolution module located between transformer layers,” constitutes a technological improvement and integrates the alleged exception into a practical application under Step 2A Prong Two. The additional elements, including a processor and an ANN comprising self-attention (transformer layers) and a graph convolution module, do not integrate the judicial exception into a practical application. The processor and ANN are recited at a high level of generality and are described as performing routine data processing functions. The claim does not specify any particularized implementation details that improve computer functionality or another technology.
While Applicant asserts improved accuracy and enhanced modeling of spatial relationships, the claim does not recite a technical mechanism by which computer performance itself is improved. Instead, the ANN is used as a tool to perform the abstract idea of classification and modeling. Further, the alleged improvement pertains to the quality of the output (e.g., improved modeling or reconstruction of body parts), which reflects an improvement to the abstract idea itself, rather than any improvement in computer functionality or to another technology or technical field. The claim does not recite any change to how the computer operates, processes data, or utilizes resources.
Additionally, the placement of a graph convolution module between transformer layers constitutes a combination of known machine learning components. As evidenced by the cited prior art (e.g., Yin et. al, Plizzari et. al), both self-attention mechanisms and graph convolutional networks were well-understood, routine, and conventional (WURC) for modeling spatial relationships and feature interactions at the time of filing. The claim does not recite how this arrangement changes the functioning of the computer itself, but rather uses known techniques for their expected purpose. Accordingly, claim 1 fails Step 2A Prong Two.
Furthermore, Applicant’s arguments regarding technological improvements are also not persuasive under Step 2B. The additional elements, individually and in combination, are WURC, including processors, ANNs, transformer/self-attention mechanisms, and graph convolutional networks. As supported by the cited references, Yin et. al demonstrates that graph convolutional networks were commonly used for human pose estimation, and Plizzari et. al demonstrates that transformer-based self-attention was widely applied in computer vision. The claimed combination merely applies these known techniques according to their established function and does not amount to significantly more than the abstract idea. Accordingly, claim 1 fails Step 2B, and the 35 USC § 101 rejection is maintained.
Additionally, Applicant argues that neither Yin, Lin, nor Plizzari teaches or suggests “a graph convolution module located between two of a plurality of transformer layers,” and therefore the Examiner has failed to establish a proper combination. As correctly noted by Applicant, Yin does not explicitly disclose the claimed arrangement, and Lin discloses multiple transformer layers but does not include a graph convolution module between them. However, Plizzari does teach an architecture in which graph convolution operations are interleaved with transformer/self-attention layers, stating that ““inside each T-TR layer, a standard graph convolution sub-module (Yan et al., 2018) is followed by the proposed Temporal Self-Attention module” (Plizzari: 4.4. Two-Stream Spatial–Temporal Transformer Network) and that the architecture is composed of multiple stacked layers combining graph convolution and transformer self-attention. Thus, Plizzari teaches a repeated, layered structure in which graph convolution and transformer components are arranged in sequence across multiple stacked layers.
Applicant’s argument improperly focuses on the absence of an explicit, verbatim disclosure of the graph convolution module being “between two transformer layers.” However, obviousness under 35 USC § 103 does not require an express teaching of the exact claimed configuration. Rather, the relevant inquiry is whether the claimed arrangement would have been obvious to one of ordinary skill in the art in view of the combined teachings. Here, Lin teaches a plurality of stacked transformer layers, and Plizzari teaches interleaving graph convolution modules with transformer/self-attention layers across multiple stacked layers. Given these teachings, a person of ordinary skill in the art would have understood that placing a graph convolution module between adjacent transformer layers represents a predictable implementation of the interleaving architecture taught by Plizzari within the multi-layer transformer framework of Lin. In other words, once multiple transformer layers are present (Lin) and graph convolution modules are interleaved with transformer layers (Plizzari), positioning a graph convolution module between two transformer layers is nothing more than the natural result of combining these teachings.
Applicant further argues that the Examiner has not shown why Yin combined with Lin and Plizzari would render the claimed features obvious. The Examiner articulated a sufficient rationale grounded in the references themselves. Plizzari teaches that interleaving graph convolution with transformer/self-attention improves modeling of spatial and temporal relationships between human body parts, and Lin teaches the use of multiple transformer layers for feature extraction and representation learning. A person of ordinary skill in the art would have been motivated to modify Yin’s system by incorporating the multi-layer transformer architecture of Lin and the interleaved graph convolution structure of Plizzari in order to improve the modeling of spatial relationships and structural dependencies in body-part representations, which is directly aligned with the purpose of the claimed invention. Such a combination constitutes a predictable use of prior art elements according to their established functions. Accordingly, the 35 USC § 103 rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The apparatus of claim 1 is directed to a machine, which is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03. However, the following elements of Claim 1 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These steps constitute mental processes because they describe acts of observation, evaluation, and judgement that a human can practically perform mentally, such as observing an image, reasoning about their spatial relationships, mentally classifying the body part, and sketching or conceptualizing a three-dimensional representation.
Determine, based on the image, a classification label and a plurality of vertices associated with the first human body part, wherein the classification label indicates a class of the first human body part, the plurality of vertices corresponds to points of the first human body part in a three-dimensional (3D) space, and the determination is made using an artificial neural network that includes a self-attention module and a graph convolution module;
And generate a first 3D model representative of the first human body part based at least on the plurality of vertices associated with the first human body part.
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception,
including a processor and artificial neural network (ANN) with a graph convolution module and self-attention module, wherein the artificial neural network includes a plurality of transformer layers and wherein the graph convolution module is located between two of the plurality of transformer layers, do not integrate the judicial exception into a practical application. Obtaining an image that depicts a first human body part is insignificant extra-solution activity (MPEP § 2106.05(g)), and the processor and ANN are described only as performing ordinary data processing operations, which are generic computer functions that do not improve the functioning of a computer or any other technology or technical field (MPEP § 2106.05(a)). Furthermore, they are computer components used as a tool amounting to instructions for applying the abstract idea (MPEP § 2106.05(f)). The claim also does not impose meaningful limits on the computer components such that the method is tied to a particular machine; the processor and ANN may operate on any generic computing system (MPEP § 2106.05(b)).
Claim 1 also fails Step 2B, as these generic elements are well-understood, routine, and conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)(III)). A processor is a generic computer element that is WURC (see MPEP § 2106.05(d)), and the ANN is also WURC, as shown by Yin et. al who states that “in recent years, many studies on human pose estimation is based on graph convolutional networks (GCNs)” (Yin: Abstract) and “self-attention [42] and its related variants have become very popular in recent years” (Yin: Attention Mechanism). Additionally, the transformer layers are also WURC, as shown by Plizzari et. al who states that “transformer self-attention has shown remarkable results on a broad range of computer vision tasks” (Plizzari: Introduction). As claim 11 contains this identical ineligible subject matter, it is also rejected.
Claims 2, 3, and 5-10 fail Step 2A Prong One as they further recite mental processes such as identifying features, determining relationships, modeling interactions, categorizing information, deriving global conclusions, aggregating information, extrapolating additional information, estimating likelihoods, and applying decision thresholds to reach conclusions. Additionally, these claims fail Step 2A Prong Two and Step 2B because the additional elements beyond the judicial exception, including a processor and an ANN with a graph convolution module and self-attention module, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above). As claims 12, 13, and 15-20 contain this identical ineligible subject matter, they are also rejected.
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.
Claims 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et. al (“Multibranch Attention Graph Convolutional Networks for 3-D Human Pose Estimation”) in view of Lin et. al (“End-to-End Human Pose and Mesh Reconstruction with Transformers”), further in view of Plizzari et. al (“Skeleton-based action recognition via spatial and temporal transformer networks”).
Regarding Claim 1, Yin teaches an apparatus, comprising: one or more processors configured to (Yin: Section IV. EXPERIMENTS):
B. Implementation Details: “The PyTorch toolbox is used to implement our proposed model and the model is evaluated with the baseline [1] and the state-of-the-art 3-D pose estimator based on graph convolution [2].”
B. Implementation Details: “All experiments were conducted on a GeForce GTX 1080 GPU with CUDA 10.0.”
obtain an image that depicts a first human body part (Yin: Fig. 1);
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determine, based on the image, a classification label and a plurality of vertices associated with the first human body part, wherein the classification label indicates a class of the first human body part, the plurality of vertices corresponds to points of the first human body part in a three-dimensional (3D) space, and the determination is made using an artificial neural network that includes a self-attention module and a graph convolution module (Yin: Abstract, Section III. METHOD, and Figs. 2 and 3 (shown below));
Abstract: “To address these issues, a novel multibranch attention graph convolution (MultiBA_GConv) operation is proposed and a regression network model for human 3-D position estimation based on the MultiBA_GConv operation is developed in this article. Several different transformation matrices are used to extract the feature information contributing to the node itself, its neighbors, and other global nodes, and the corresponding attention mechanism is used to focus on these features in the MultiBA_GConv operation.”
III. METHOD: “Based on this operation, a network architecture for pose regression is developed, which takes a single image with 2-D human position coordinates as input and predicts the corresponding 3-D human position coordinates.”
A. Human Body Graphic Model: “Human pose can be represented as a skeletal graphic model consisting of multiple body joints connected by bones, with joints serving as nodes and bones as edges. Both 2-D and 3-D pose can be naturally represented by the skeletal graphic model in the form of 2-D or 3-D coordinates. In this article, a human skeleton graphical model with 16 nodes and 19 edges is used, as depicted in Fig. 2(a).”
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and generate a first 3D model representative of the first human body part based at least on the plurality of vertices associated with the first human body part (Yin: Section III. METHOD (shown above) and Fig. 1 (shown above)).
Yin fails to teach that the artificial neural network includes a plurality of transformer layers and the graph convolution module is located between two of the plurality of transformer layers. Lin discloses a transformer-based self-attention mechanism with multiple encoder layers, stating that “given a set of joint queries and vertex queries, we perform self-attentions through multiple layers of a transformer encoder, and regress the 3D coordinates of body joints and mesh vertices in parallel” (Lin: Fig. 2). However, Lin fails to teach that there is a graph convolution module located between the two transformer layers of the artificial neural network.
However, Plizzari teaches an architecture in which graph convolution modules are structurally interleaved with transformer self-attention layers, stating that “inside each T-TR layer, a standard graph convolution sub-module (Yan et al., 2018) is followed by the proposed Temporal Self-Attention module” (Plizzari: 4.4. Two-Stream Spatial–Temporal Transformer Network) and that the architecture is composed of multiple stacked layers combining graph convolution and transformer self-attention.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Yin by incorporating the stacked transformer architecture of Lin and arranging the graph convolution module as taught by Plizzari. Plizzari teaches that interleaving graph convolution and transformer self-attention improves the modeling of spatial and temporal relationships between human body parts, and Lin teaches that multiple transformer layers are effective for capturing global dependencies in human body representations. Combining these teachings involves the predictable use of known architectural arrangements to achieve improved feature representation.
Regarding Claim 2, Yin in view of Lin and Plizzari teaches the apparatus of claim 1, and Yin further teaches that the self-attention module is configured to receive a representation of local features of the image and determine a plurality of global features of the image based on the local features, the plurality of global features indicating an interrelationship of the plurality of vertices (Yin: Section III. METHOD and Figs. 2b and 3 (shown above)).
Global Attention and Global Features Aggregation: “To capture this information, a learnable global attention (GA) matrix G (l) ∈ R N×N is introduced in this article. Similarly, the GA matrix is normalized and the normalized global relationship matrix is represented as follows…However the coordination relationship between human pose is often reflected in body parts as a whole, rather than individual joints alone. So the GA in our experiment focuses on the human body parts information.”
Global Attention and Global Features Aggregation: “H (l) global is the final global features aggregated from all parts of the human body.”
Regarding Claim 3, Yin in view of Lin and Plizzari teaches the apparatus of claim 2, and Yin further teaches that the graph convolution module is configured to:
receive the representation of the local features; extract, from the local features, information that indicates local interactions of the plurality of vertices (Yin: INTRODUCTION and Fig. 3 (shown above));
“Graph convolutional network (GCN) or local linkage network is used to extract local features between connected joints to make full use of the linkage relationship between joints.”
and refine the plurality of global features determined by the self-attention module with the extracted information (Yin: Fig. 3 (shown above)).
Regarding Claim 5, Yin in view of Lin and Plizzari teaches the apparatus of claim 3, and Yin further teaches that the graph convolution module is configured to model the local interactions of the plurality of vertices via a graph that comprises nodes and edges, each of the nodes corresponding to a respective vertex of the plurality of vertices, each of the edges connecting two nodes and representing an interaction between the vertices corresponding to the two nodes (Yin: Section III. METHOD (shown above) and Fig. 2a (shown above)).
Regarding Claim 6, Yin in view of Lin and Plizzari teaches the apparatus of claim 2, and Yin further teaches that the artificial neural network further includes a convolutional neural network configured to extract the local features from the image (Yin: Fig. 1 (shown above)).
Regarding Claim 7, Yin in view of Lin and Plizzari teaches the apparatus of claim 2, but Yin does not teach that the self-attention module is configured to receive the representation of the local features as a sequence of tokens, project the sequence of tokens into a query vector, a key vector, and a value vector, and determine the plurality of global features based on the query vector, the key vector, and the value vector.
However, Lin teaches using a transformer-based self-attention mechanism to process joint and vertex-level features, enabling each token to attend to all others regardless of spatial distance. Lin states that “the input to the transformer encoder are the body joint and mesh vertex queries” (Lin: 3.2. Multi-Layer Transformer Encoder with Progressive Dimensionality Reduction) and that the transformer “models global interactions among joints and mesh vertices without being limited by any mesh topology….in addition, our method learns with self-attention mechanism, which is different from prior studies” (Lin: 2. Related Works). As a transformer encoder performs linear projections of the input tokens into query, key, and value vectors for multi-head self-attention, Lin further discloses determining global features by aggregating information across all tokens through the attention mechanism.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Yin’s self-attention-based global feature determination to use Lin’s transformer self-attention with query, key, and value projections because Lin teaches that token-based self-attention enables global dependency modeling across all joints or vertices, allowing each element to attend to all others regardless of spatial distance. Applying Lin’s transformer self-attention to Yin’s system would predictably improve the modeling of long-range relationships among vertices while still determining global features from local features, which is a known goal in human pose and mesh reconstruction systems.
Regarding Claim 8, Yin in view of Lin and Plizzari teaches the apparatus of claim 1, but Yin does not teach that one or more processors are further configured to determine a classification label and a plurality of vertices associated with a second human body part, generate a second 3D model that represents the second human body part based at least on the plurality of vertices associated with the second human body part, and further generate a full-body 3D model based at least on the first 3D model, the second 3D model, and the respective classification labels associated with the first human body part and the second human body part.
However, Lin teaches a transformer-based framework that simultaneously predicts 3D joints corresponding to different human body parts and mesh vertices, and generates a full-body 3D mesh from a single image. Lin states that their method “outputs 3D joint coordinates and mesh vertices simultaneously” (Lin: Abstract) and that “given a set of joint queries and vertex queries, we perform self-attentions through multiple layers of a transformer encoder, and regress the 3D coordinates of body joints and mesh vertices in parallel” (Lin: Fig. 2). A full human mesh is then reconstructed by modeling non-local interactions among joints and mesh vertices, including joints and vertices belonging to different body parts such as R-wrist, R-elbow, L-knee, L-ankle, and head (Lin: Fig. 3).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Lin’s method into Yin’s multibranch attention graph convolution operation. As explained by Lin, modeling interactions among joints and vertices belonging to different body parts is valuable because “researchers have shown that there are strong correlations between non-local vertices which may belong to different parts of the body (e.g. hand and foot),” (Lin: Introduction) and learning these correlations improves robustness to pose variation and occlusion.
Regarding Claim 9, Yin in view of Lin and Plizzari teaches the apparatus of claim 8, but Yin does not teach one or more processors being configured to generate the full-body 3D model comprises the one or more processors being configured to up-sample the plurality of vertices associated with the first human body part and the plurality of vertices associated with the second human body part to a number of vertices required by a parametric human model, and generate the full-body 3D model based on the up-sampled number of vertices and the parametric human model.
However, Lin teaches generating a coarse mesh representation and then up-sampling the mesh vertices to a higher-resolution human mesh topology, stating that “we use a coarse template mesh (431 vertices) for positional encoding, and transformer outputs a coarse mesh; (2) We use learnable Multi-Layer Perceptrons (MLPs) to upsample the coarse mesh to the original mesh (6890 vertices for SMPL human mesh topology)” (Lin: 3.5. Implementation Details).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Lin’s method into Yin’s multibranch attention graph convolution operation. Lin explains that this up-sampling approach is motivated by computational efficiency and redundancy reduction, while still producing a dense human mesh suitable for full-body reconstruction, stating that “the implementation of learning a coarse mesh followed by upsampling is helpful to reduce computation…it also helps avoid redundancy in original mesh (due to spatial locality of vertices), which makes training more efficient” (Lin: 3.5. Implementation Details). A person of ordinary skill in the art would have been motivated to incorporate this method into Lin and Yin’s teachings in order to improve computational and training efficiency.
Regarding Claim 10, Yin in view of Lin and Plizzari teaches the apparatus of claim 1, but both Yin and Lin fail to teach that one or more processors are further configured to determine a classification label probability that indicates of a likelihood that the first human body part belongs to the class indicated by the classification label, and wherein the one or more processors are configured to generate the first 3D model representative of the first human body part further based on a determination that the classification label probability is above a threshold value.
However, Plizzari teaches producing classification probability scores using a softmax classifier, stating that “a global average pooling layer is applied before the softmax classifier and each stream is trained using the standard cross-entropy loss,” (Plizzari: 5.3. Experimental settings) and that “the sub-networks outputs are eventually fused together by summing up their softmax output scores to obtain the final prediction” (Plizzari: 4.4. Two-Stream Spatial–Temporal Transformer Network). Because Plizzari relies on probability scores to control which predictions are accepted as final outputs, Plizzari emphasizes prediction confidence and reliability as a design consideration.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Plizzari’s classification probability scores into Yin and Lin’s system. Because of Plizzari’s reliance on probability scores to determine valid predictions, a person of ordinary skill in the art would have been motivated to condition the generation or acceptance of a downstream output, such as a 3D model, on whether a classification label probability exceeds a threshold, in order to avoid propagating low-confidence predictions and to improve robustness of the generated output.
Regarding Claim 11, Yin in view of Lin and Plizzari teaches all of the limitations of Claim 1 above because Claim 11 recites a method comprising steps that correspond in substance to the functions of the apparatus of Claim 1.
Regarding Claim 12, Yin in view of Lin and Plizzari teaches the method according to Claim 11, and additional limitations are met as in the consideration of Claim 2 above.
Regarding Claim 13, Yin in view of Lin and Plizzari teaches the method according to Claim 12, and additional limitations are met as in the consideration of Claim 3 above.
Regarding Claim 15, Yin in view of Lin and Plizzari teaches the method according to Claim 13, and additional limitations are met as in the consideration of Claim 5 above.
Regarding Claim 16, Yin in view of Lin and Plizzari teaches the method according to Claim 12, and additional limitations are met as in the consideration of Claim 6 above.
Regarding Claim 17, Yin in view of Lin and Plizzari teaches the method according to Claim 12, and additional limitations are met as in the consideration of Claim 7 above.
Regarding Claim 18, Yin in view of Lin and Plizzari teaches the method according to Claim 11, and additional limitations are met as in the consideration of Claim 8 above.
Regarding Claim 19, Yin in view of Lin and Plizzari teaches the method according to Claim 18, and additional limitations are met as in the consideration of Claim 9 above.
Regarding Claim 20, Yin in view of Lin and Plizzari teaches the method according to Claim 11, and additional limitations are met as in the consideration of Claim 10 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shahid (“Skeleton-Based Action Recognition with Adaptive and Self-Attentive Graph Convolution Network”) teaches a new class of GCN, adaptive local and global context-aware, and spatiotemporal self-attentive GCN, for skeleton-based action recognition. The adaptive and self-attentive graph convolution neural network (ASA-GCN) is capable of focusing on the important joints and bones in each frame by using local and global adaptive graph topology. To further improve the capability, spatial and temporal self-attention graph mechanism is also introduced, with which the attention performance of the network is enhanced progressively.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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