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 .
Response to Arguments
Applicant on page 4 of the “Remarks” asserts “Because Woo is silent on a three-dimensional CIS configured for processing data including non-contrast computed tomography medical images, optionally non-contras computed tomography medical images, and Ling expressly teaches away from such a three-dimensional CIS, a person of ordinary skill in the art, upon reading Woo and Ling would be led in a direction divergent from the path that was taken by the Applicants”.
Response: Applicant’s argument is not persuasive. Ling on page 03 expressly discloses “a 3D model that feeds volumetric data as input” and further teaches that “basic model, which only uses non-enhanced phase CT images as input”, a “non-contrast computed tomography” of claimed limitation aligns with "non-enhanced phase CT, thereby directly disclosing a three-dimensional classification system configured to process non-contrast CT image. Although Ling also reports that the enhanced model performs better and that certain HCC criteria such as washout cannot be extracted without contrast enhanced slices, such comparative performance discussion does not amount to teaching away. A reference teaches away only when it criticizes, discredits, or otherwise discourages the claimed solution; mere disclosure that one alternative performs better than another does not teach away. Here, Ling does not discourage use of non-enhanced CT as input, but instead expressly uses and evaluates such input. Thus, Ling teaches the amended limitation, and Woo continues to teach the recited CBAM having channel and spatial attention modules. Therefore, applicant’s argument is not persuasive.
The same reasoning also undercuts applicant’s derivative arguments for claims 14, 15, 18, and 19-25.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-13, 16-17 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Woo et al. “CBAM: Convolutional Block Attention Module” in view Ling et al. “Automatic volumetric diagnosis of hepatocellular carcinoma based on four-phase CT scans with minimum extra information”.
Regarding claim 1, Woo et al. disclose comprising one or more Convoluted Block Attention Module (CBAM) blocks (see page 2, 2nd para; “we propose a new network module, named “Convolutional Block Attention Module””), wherein at least one of the one or more CBAM blocks comprises a CBAM (see page 6, section 4 Experiment; “One can seamlessly integrate CBAM in any CNN architectures and jointly train the combined CBAM-enhanced networks. Fig. 3 shows a diagram of CBAM integrated with a ResBlock in ResNet [5] as an example”), wherein the CBAM comprises a channel attention module and a spatial attention module (see Fig. 1; “The overview of CBAM. The module has two sequential sub-modules: channel and spatial”, see also page 2, 2nd para; “we sequentially apply channel and spatial attention modules”). Woo et al. does not teach a three-dimensional computer-implemented classification system, wherein the three-dimensional CIS is configured for processing data comprising non- contrast computed tomography images, optionally non-contrast computed tomography medical images
In the same field of endeavor Ling et al. teaches a three-dimensional computer-implemented classification system (CIS) (see page 5, Fig. 2, “The 3D ResNet in CT pathway contains 14 layers”, see also page 09, “Discussion”; “We used volumetric 3D CT patches as inputs. The 3D model can provide more relevant information to lesion classification” Note: these passages establish a 3D, computer implemented classification system on volumetric CT), wherein the three-dimensional CIS is configured for processing data comprising non- contrast computed tomography images (see page 3, left col, 4th para; “the diagnosis results of the basic model, which only uses non-enhanced phase CT images as input” Note; non-contrast computed tomography is non-enhanced phase CT), optionally non-contrast computed tomography medical images (see page 9, “Discussion”; “In this work, we built a deep learning-based model, MExPaLe, for the diagnosis of liver tumor with typical images from four phase CT ”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. in order to demonstrate high performance and excellent efficiency (see Fig. 2).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Woo et al.in the combination further teaches wherein the channel attention module is proceeded by the spatial attention module within the CBAM (see page 2, 2nd para; “we sequentially apply channel and spatial attention modules (as shown in Fig. 1)”).
Regarding claim 3, the rejection of claim 1 is incorporated herein.
Woo et al. in the combination further teach wherein the channel attention module comprises two parallel pathways which first pass through a global average pooling layer and a global max pooling layer respectively (see page 5, Fig. 2; “the channel sub-module utilizes both max-pooling outputs and average-pooling outputs with a shared network”).
Regarding claim 4, the rejection of claim 3 is incorporated herein.
Woo et al. in the combination further teach wherein, within the channel attention module, each of the two parallel pathways comprises two fully connected layers (see Fig. 2; the shared MLP for channel attention has two FC layers (a bottleneck), applied to both pooled descriptors-routine implementation mirrored across the two paths).
Regarding claim 5, the rejection of claim 4 is incorporated herein.
Woo et al. in the combination further teach wherein the channel attention module comprises an addition layer that combines the outputs from each of the two parallel pathways (see page 5, 2nd para; “After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation… we merge the output feature vectors using element-wise summation”).
Regarding claim 6, the rejection of claim 5 is incorporated herein.
Woo et al. in the combination further teach wherein the channel attention module further comprises a first activation function (see page 5, last para; “the ReLU activation function is followed by W0”).
Regarding claim 7, the rejection of claim 6 is incorporated herein.
Woo et al. in the combination further teach wherein the first activation function is selected from a sigmoid activation function, a rectified linear unit activation function (ReLu) layer, and/or a parametric rectified linear unit activation function (PReLu) layer (see page 5, last para; “where σ denotes the sigmoid function, W0 ∈ R C/r×C, and W1 ∈ R C×C/r. Note that the MLP weights, W0 and W1, are shared for both inputs and the ReLU activation function is followed by W0”).
Regarding claim 8, the rejection of claim 1 is incorporated herein.
Woo et al. in the combination further teach wherein the spatial attention module comprises a first pooling layer and a second pooling layer, optionally wherein the first pooling layer and the second pooling layer are an average pooling layer and a max pooling layer, respectively (see Fig. 2 disclose “the channel sub-module utilizes both max-pooling outputs and average-pooling outputs with a shared network”, page 9, 2nd para; “we use the average- and max-pooled features across the channel axis with a convolution kernel size of 7 as our spatial attention module”).
Regarding claim 9, the rejection of claim 8 is incorporated herein.
Woo et al. in the combination further teach wherein the spatial attention module comprises a concatenation layer and a 3D convolutional layer after the average pooling layer and the max pooling layer (see page 6 1st para; “we first apply average-pooling and max-pooling operations along the channel axis and concatenate them to generate an efficient feature descriptor… On the concatenated feature descriptor, we apply a convolution layer”).
Regarding claim 10, the rejection of claim 9 is incorporated herein.
Woo et al. in the combination further teach wherein the spatial attention module further comprises a second activation function, after the 3D convolutional layer (see page 8, last para; “The final attention map is normalized by the sigmoid function”).
Regarding claim 11, the rejection of claim 10 is incorporated herein.
Woo et al. in the combination further teach wherein the second activation function is selected from a sigmoid activation function, a rectified linear unit activation function (ReLu) layer, and/or a parametric rectified linear unit activation function (PReLu) layer (see page 8, last para; “The final attention map is normalized by the sigmoid function”).
Regarding claim 12, the rejection of claim 1 is incorporated herein.
Woo et al. in the combination further teach wherein the CBAM is preceded by a convolutional layer (see page 7, Fig.3; “We apply CBAM on the convolution outputs in each block”).
Regarding claim 13, the rejection of claim 12 is incorporated herein.
Woo et al. in the combination further teach wherein the third activation function is selected from a rectified linear unit activation function ReLu layer, a parametric rectified linear unit activation function ReLu layer, and/or a sigmoid activation function layer (see page 5, last para; “Note that the MLP weights, W0 and W1, are shared for both inputs and the ReLU activation function is followed by W0”, see also page 8, last para; “The final attention map is normalized by the sigmoid function”).
Regarding claim 16, the rejection of claim 1 is incorporated herein.
Woo et al. in the combination further teach wherein at least one of the one or more CBAM blocks comprises two or more CBAMs, each CBAM comprising the channel attention module and the spatial attention module (see page 4, 4th para; “They place BAM module at every bottleneck of the network while we plug at every convolutional block”, see also page 13, section 4.6; “We then place SE [28] and CBAM right before every classifier” Note: thus, teaching multiple CBAM equipped blocks).
Regarding claim 17, the rejection of claim 1 is incorporated herein.
Woo et al. in the combination further teach comprising two or more CBAM blocks (see page 4, 4th para; “They place BAM module at every bottleneck of the network while we plug at every convolutional block”, see also page 13, section 4.6; “We then place SE [28] and CBAM right before every classifier” Note: thus, teaching multiple CBAM equipped blocks).
Regarding claim 35, Woo et al. disclose comprising one or more Convoluted Block Attention Module (CBAM) blocks (see page 2, 2nd para; “we propose a new network module, named “Convolutional Block Attention Module””), wherein at least one of the one or more CBAM blocks comprises a CBAM (see page 6, section 4 Experiment; “One can seamlessly integrate CBAM in any CNN architectures and jointly train the combined CBAM-enhanced networks. Fig. 3 shows a diagram of CBAM integrated with a ResBlock in ResNet [5] as an example”), wherein the CBAM comprises a channel attention module and a spatial attention module (see Fig. 1; “The overview of CBAM. The module has two sequential sub-modules: channel and spatial”, see also page 2, 2nd para; “we sequentially apply channel and spatial attention modules”), and at least one CBAM comprises a three-dimensional spatial attention module (see page 6, 1st para; “Instead of directly computing the 3D attention map, we decompose the process that learns channel attention and spatial attention separately. The separate attention generation process for 3D feature map has much less computational and parameter over head”). Woo et al. does not teach a three-dimensional computer-implemented classification system, wherein the three-dimensional CIS comprises a three-dimensional neural network.
In the same field of endeavor Ling et al. teaches a three-dimensional computer-implemented classification system (CIS) (see page 5, Fig. 2, “The 3D ResNet in CT pathway contains 14 layers”, see also page 09, “Discussion”; “We used volumetric 3D CT patches as inputs. The 3D model can provide more relevant information to lesion classification” Note: these passages establish a 3D, computer implemented classification system on volumetric CT), wherein the three-dimensional CIS comprises a three-dimensional neural network (see page 1, “Materials and methods”; “A deep learning model using 3D convolutional neural network (CNN)”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. in order to demonstrate high performance and excellent efficiency (see Fig. 2).
Claims 14-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Woo et al. in view of Ling et al. as applied in claim 1 above and further in view of Wang et al. “RDAU-Net: Based on a Residual Convolutional Neural Network with DFP and CBAM for Brain Tumor Segmentation”.
Regarding claim 14, the rejection of claim 1 is incorporated herein.
The combination of Woo et al. and Ling et al. as a whole does not teach wherein the CBAM is proceeded by a normalization layer.
In the same field of endeavor Wang et al. teach wherein the CBAM is proceeded by a normalization layer (see page 4, section “RA Block”; “We propose a residual block with a 3D CBAM, namely, an RA block that is composed of two 3×3×3 convolutional layers for the pink module, two normalization layers for the purple module”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and automatic segmentation of brain tumors based on RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks of Wang et al. in order to improve the segmentation accuracy of the network (see page 4).
Regarding claim 15, the rejection of claim 14 is incorporated herein.
Wang et al. in the combination further teach wherein the normalization layer is selected from a batch normalization layer, a weight normalization layer, a layer normalization layer, an instance normalization layer, a group normalization layer, a batch renormalization layer, and/or a batch-instance normalization layer (see page 4, section “RA Block”; “we substitute the instance normalization (IN) and leaky rectified linear unit (LR) functions for the popular batch normalization (BN) and rectified linear unit (ReLU) functions, respectively”).
Regarding claim 18, the rejection of claim 1 is incorporated herein.
Wang et al. in the combination further teach comprising a CBAM block and two fully connected layers, arranged together in series configuration (see page 4, section “RA Block”; “We propose a residual block with a 3D CBAM, namely, an RA block that is composed of two 3×3×3 convolutional layers for the pink module, two normalization layers for the purple module, two activation layers for the yellow module, and an attention layer for the green module, as illustrated in Figure 3”), wherein the CBAM block contains two CBAMs (see Fig. 3 and Fig. 4), each comprising a channel attention module and a spatial attention module, arranged in series configuration (see Fig. 2), and wherein each CBAM is preceded by a convolutional layer containing a ReLU activation function, and followed by a batch normalization layer (see page 4, section “RA Block”; “we substitute the instance normalization (IN) and leaky rectified linear unit (LR) functions for the popular batch normalization (BN) and rectified linear unit (ReLU) functions, respectively”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and automatic segmentation of brain tumors based on RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks of Wang et al. in order to achieves state-of-the-art performance (see page 4).
Claims 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Woo et al. in view of Ling et al. as applied in claim 1 above and further in view of He et al. NPL “Deep Residual Learning for Image Recognition”.
Regarding claim 19, the rejection of claim 1 is incorporated herein.
The combination of Woo et al. and Ling et al. as a whole does not teach wherein adjacent CBAM blocks are operably linked by a transitional layer.
In the same field of endeavor, He et al. teaches wherein adjacent CBAM blocks are operably linked by a transitional layer (see page 772 section 3.3; “We perform downsampling directly by convolutional layers that have a stride of 2”, see also page 774, Table 1; “Downsampling is performed by conv3 1, conv4 1, and conv5 1 with a stride of 2” Note: by definition down sampling can be accomplished by using a transitional layer). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and deep residual learning for image recognition of He et al. in order to ease the training of networks that are substantially deeper than those used previously (see page 772).
Regarding claim 20, the rejection of claim 19 is incorporated herein.
He et al. in the combination further teach wherein the transitional layer comprises a pooling layer (see page 774, Table 1; disclose “3x3 max pool, stride 2”, Fig. 3 left/middle, pooling with sride-2 shown at stage transitions between adjacent blocks).
Regarding claim 21, the rejection of claim 20 is incorporated herein.
He et al. in the combination further teach wherein the pooling layer comprises a max pooling layer or an average pooling layer (see page 772 section 3.3; “The network ends with a global average pooling layer and a 1000-way fully-connected layer with softmax”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and deep residual learning for image recognition of He et al. in order to gain a good generalization performance on recognition tasks (see page 772).
Regarding claim 22, the rejection of claim 20 is incorporated herein.
He et al. in the combination further teach wherein the pooling layer has a stride size of (2, 2, 1) or (3, 3, 3) (see page 774, Table 1; “Downsampling is performed by conv3 1, conv4 1, and conv5 1 with a stride of 2”).
Regarding claim 23, the rejection of claim 1 is incorporated herein.
He et al. in the combination further teach further comprising a classification layer operably linked to a terminal CBAM block of the at least one or more CBAM blocks (see Fig. 3, “fc 4069…fc 4096….fc 1000” page 772 section 3.3; “The network ends with a global average pooling layer and a 1000-way fully-connected layer with softmax”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and deep residual learning for image recognition of He et al. in order to gain accuracy from considerably increased depth (see Fig. 3).
Regarding claim 24, the rejection of claim 23 is incorporated herein.
He et al. in the combination further teach wherein the classification layer comprises a flattening layer and the two fully connected layers (see page 3, Fig. 1; “The intermediate feature map is adaptively refined through our module (CBAM) at every convolutional block of deep networks”, see also para 4, 4th para; “They place BAM module at every bottleneck of the network while we plug at every convolutional block”).
Regarding claim 25, the rejection of claim 1 is incorporated herein.
He et al. in the combination further teach wherein convolutional layers in subsequent CBAM blocks contain progressively more kernels than convolutional layers in prior CBAM blocks (see page 772, section 3.3; “(i) for the same output feature map size, the layers have the same number of filters; and (ii) if the feature map size is halved, the number of filters is doubled”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and deep residual learning for image recognition of He et al. in order to gain accuracy from considerably increased depth (see Fig. 3).
Claims 26-28, and 30-34 are rejected under 35 U.S.C. 103 as being unpatentable over Ling et al. in view of Woo et al. as applied in claim 1 above and further in view of Brynolfsson et al. (US 20220005586 A1).
Regarding claim 26, the rejection of claim 1 is incorporated herein. Ling et al. in the combination further tech a computer-implemented method (CIM) for processing, analyzing, and/or recognizing data (see page 2, right col. 2nd para; “deep learning with convolutional neural network (CNN) have achieved state-of-the-art performances with respect to pattern recognition of images for various organs and tissues”). However, the combination of Woo et al. and Ling et al. as a whole does not teach the CIM involving visualizing on a graphical user interface, output from the CIS.
In the same field of endeavor Brynolfsson et al. teaches the CIM involving visualizing on a graphical user interface, output from the CIS (see Fig. 5Apara [0012]; “anatomical labeling, are displayed with an interactive graphical user interface”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a module sequentially infers attention maps along two separate dimensions, channel and spatial for adaptive feature refinement of Woo et al. in view of the use of a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI of Ling et al. and AI-based lesion detection, segmentation, and classification of Brynolfsson et al. in order to provide estimation of disease severity and risk gain (see para [0002]).
Regarding claim 27, the rejection of claim 26 is incorporated herein.
Woo et al. in the combination further teach wherein outputs from the parallel pathways within the channel attention module are combined and transmitted through the channel attention module’s activation function, optionally wherein the activation function is a sigmoid activation function (see page 9, 3rd para; “combine two attention outputs to build a 3D attention map. In the case, both attentions can be applied in parallel, then the outputs of the two attention modules are added and normalized with the sigmoid function”).
Regarding claim 28, the rejection of claim 26 is incorporated herein.
Woo et al. in the combination further teach wherein input to the channel attention module is combined with output from the channel attention module’s activation function, and transmitted as input to the spatial attention module or (ii) input to the spatial attention module is combined with output from the spatial attention module's activation function, and transmitted as input to a subsequent layer in the CBAM block (see Fig. 1; “The overview of CBAM. The module has two sequential sub-modules: channel and spatial”, see also page 4, section 3; “The overall attention process can be summarized as: F ′ = Mc(F) ⊗ F, F ′′ = Ms(F ′ ) ⊗ F ′ , (1) where ⊗ denotes element-wise multiplication” see also page 3, Fig. 1; “The intermediate feature map is adaptively refined through our module (CBAM) at every convolutional block of deep networks”, Note: sequential spatial attention applied after channel).
Regarding claim 30, the rejection of claim 26 is incorporated herein.
Ling et al. in the combination further teach wherein the data are non-contrast computed tomography (CT) medical images (see page 1, 3rd para; “This clinical retrospective study uses CT scans of liver tumors over four phases (non-enhanced phase, arterial phase, portal venous phase, and delayed phase”).
Regarding claim 31, the rejection of claim 26 is incorporated herein.
Ling et al. in the combination further teach wherein the data are non-contrast CT medical images of intra-abdominal organs or intra-abdominal tissues (see page 1, 3rd para; “This clinical retrospective study uses CT scans of liver tumors over four phases”, see also para 3, 4th para; “the diagnosis results of the basic model, which only uses non-enhanced phase CT images as input”).
Regarding claim 32, the rejection of claim 26 is incorporated herein.
Ling et al. in the combination further teach wherein the data are non-contrast CT liver scans (see page 1, 3rd para; “This clinical retrospective study uses CT scans of liver tumors over four phases” see also para 3, 4th para; “the diagnosis results of the basic model, which only uses non-enhanced phase CT images as input”).
Regarding claim 33, the rejection of claim 26 is incorporated herein.
Brynolfsson et al. in the combination further teach wherein visualizing the output on the graphical user interface, provides a diagnosis, prognosis, or both, of a disease or disorder in a subject (see para [0012]; “the GUI may also allow users to identify, and segment… Once a user is satisfied… generate a final, signed, report that can, for example, be reviewed and used to discuss outcomes and diagnosis with a patient, and assess prognosis and treatment options”).
Regarding claim 34, the rejection of claim 26 is incorporated herein.
Ling et al. in the combination further teach wherein the disease or disorder is hepatocellular carcinoma (see page 1, Objective”; “The aim of this study was to develop a deep-learning-based model for the diagnosis of hepatocellular carcinoma”).
Conclusion
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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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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