DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. Claim 10 is objected to because of the following informalities: “after the generating…” found in the first line of the claim is grammatically incorrect. Appropriate correction is required.
Claim Rejections - 35 USC § 103
3. 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.
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ansari (WO 2023288262 A1) in view of Kentaro (GB 2593717 A).
With respect to claim 1, Ansari teaches a method for controlling autonomous driving of a vehicle, the method comprising:
outputting, by a depth network, an inference depth from a sequence image (“Initially, a subject frame of image data at time t ( I.sub.t ) 202 is provided to a machine learning depth model 204, such as a monocular depth-estimating artificial neural network model (referred to in some examples as “DepthNet”). Depth model 204 processes the image data and generates an estimated depth output ( D.sub.t ) 206.” Paragraph 0032);
outputting, by a pose network and based on the sequence image, an initial inference pose (“Pose estimation function 220 is generally configured to perform pose estimation, which may include determining a projection from one frame to another. The pose estimation function 220 can use any suitable techniques or operations to generate the pose estimates, such as using a trained machine learning model (e.g., a pose network). In an aspect, the pose estimate (also referred to as a relative pose or relative pose estimate in some aspects) generally indicates the (predicted) pose of objects, relative to the imaging sensor (e.g., relative to the ego vehicle). For example, the relative pose may indicate the inferred location and orientation of objects relative to the ego vehicle (or the location and orientation of the imaging sensor relative to one or more object(s)).” Paragraph 0037);
generating, based on a synthetic depth, a dynamic mask, wherein the synthetic depth is generated based on the inference depth and the initial inference pose, and the inference depth (“The estimated depth output 206 can take different forms, such as a depth map indicating the estimated depth of each pixel directly” paragraph 0032);
based on the sequence image, the inference depth, and the inference pose, training (“In some aspects, method 400 further includes generating an estimated image frame (e.g., reconstructed subject frame 222 in FIG. 2) based on the depth output, one or more context frames (e.g., context frames 216 in FIG. 2), and a pose estimate (e.g., as produced by pose estimation function 220 in FIG. 2); and determining a photometric loss (e.g., as produced by photometric loss function 224 in FIG. 2) for the depth model based on the estimated image frame and the input image frame, wherein the photometric loss is another component of the multi-component loss function (e.g., final loss function 205 in FIG. 2)” paragraph 0063)
a synthetic image model comprising the depth network and the pose network to generate a synthetic image (“The estimated depth output 206 is also provided as an input to view synthesis function 218. View synthesis function 218 further takes as inputs one or more context frames ( I.sub.s ) 216 and a pose estimate from pose estimation function 220 and generates a reconstructed subject frame (/.sub.t) 222.” Paragraph 0035);
outputting a signal associated with the synthetic image (“Moreover, aspects described herein overcome limitations of conventional depth estimation techniques by generating additional supervisory signals related to objects that are not static in the scenery.” Paragraph 0026); and
controlling, based on the signal, autonomous driving of the vehicle (“This depth output 206 can then be used for a variety of purposes, such as autonomous driving and/or driving assistance, as discussed above.” Paragraph 0050).
Ansari does not teach generating, by the pose network and based on the sequence image and the dynamic mask, a refined inference pose;
Kentaro teaches teach generating, by the pose network and based on a sequence image and depth information (“…the estimated pose of each given object is transformed to a common reference frame (for example, using information indicative of the position and orientation of the sensors 106 when capturing the image and associated depth information)” page 18 lines 26-29), a refined inference pose (“Having determined an estimate for the pose of each of the plurality of objects, and transformed the estimated poses to a common reference frame if necessary, the system 100 performs an iterative pose refinement routine to jointly optimise the estimated poses as described hereafter. The pose refinement routine starts at 706, where the system 100 samples a set of points from a stored model of each given object, transformed in accordance with the corresponding estimated pose of the object. In this example, the stored model is a volumetric solid model and the system 100 samples the set of points uniformly from throughout the volume of the volumetric solid model.” Page 18 (last 3 lines- 19 (lines1-5));
Kentaro is analogous art in the same field of endeavor as the claimed invention. Kentaro is directed towards depth estimation and pose prediction (“The present invention relates to processing image data and associated depth information to determine poses of objects in a three-dimensional scene.” Page 1 Technical Field). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Ansari with Kentaro by utilizing the prose prediction strategy of Kentaro inside the broader synthetic image generation method of Ansari, with the expectation that doing so would lead to improvement in accuracy for detecting objects in contact with each other or partially obscured (“By using a combination of pointwise feature data for points on the surface of the given object with occupancy data indicating surrounding regions occupied free space and other objects, the estimated pose is made dependent on detailed visual information relating to the given object, whilst also taking into account information relating to the surroundings of the given object As a result, the accuracy of pose estimation is improved, especially in cluttered or densely populated scenes.” Page 4 paragraph 3).
With respect to claim 2, Ansari and Kentaro teach the method of claim 1. Ansari further teaches wherein the dynamic mask is generated as a depth map (“The estimated depth output 206 can take different forms, such as a depth map indicating the estimated depth of each pixel directly…” paragraph 0032) representing a dynamic region, wherein the dynamic region is determined based on a difference between the synthetic depth and the inference depth (“The estimated depth output 206 can take different forms, such as a depth map indicating the estimated depth of each pixel directly, or a disparity map indicating the disparity between pixels. As discussed above, depth and disparity are related and can be proportionally derived from each other.” Paragraph 0032).
With respect to claim 3, Ansari and Kentaro teach the method of claim 1. Ansari further teaches wherein the dynamic mask is a depth map (“The estimated depth output 206 can take different forms, such as a depth map indicating the estimated depth of each pixel directly…” paragraph 0032), wherein the depth map is generated to match a size of the sequence image based on having a spatial dimension of a feature (“Initially, a subject frame of image data at time t ( I.sub.t ) 202 is provided to a machine learning depth model 204, such as a monocular depth-estimating artificial neural network model (referred to in some examples as “DepthNet”). Depth model 204 processes the image data and generates an estimated depth output ( D.sub.t ) 206. The estimated depth output 206 can take different forms, such as a depth map indicating the estimated depth of each pixel directly” paragraph 0032), wherein the depth map is applied to the feature to filter out a dynamic region from the feature, wherein the feature is extracted from the sequence image (“In some cases, a CNN may enable processing of digital images with minimal pre-processing. A CNN may be characterized by the use of convolutional (or cross-correlational) hidden layers. These layers apply a convolution operation to the input before signaling the result to the next layer. Each convolutional node may process data for a limited field of input (i.e., the receptive field). During a forward pass of the CNN, filters at each layer may be convolved across the input volume, computing the dot product between the filter and the input. During the training process, the filters may be modified so that they activate when they detect a particular feature within the input.” Paragraph 0081).
With respect to claim 4, Ansari and Kentaro teach the method of claim 1. Ansari further teaches wherein the sequence image comprises a source image and a target image related to the source image in time series (“Initially, a subject frame of image data at time t ( I.sub.t ) 202 is provided to a machine learning depth model 204” paragraph 0032 and “The context frames 216 may generally comprise frames near to the subject frame 202. For example, context frames 216 may be some number of frames or time steps on either side of subject frame 202, such as t +/- 1 (adjacent frames), t +/- 2 (non-adjacent frames), or the like. Though these examples are symmetric about subject frame 202, context frames 216 could be non-symmetrically located, such as / - 1 and t + 3.” Paragraph 0036),
wherein the inference depth comprises a source inference depth generated based on the source image (“Using the training architecture 200, the depth model 204 thereby learns to generate improved and more-accurate depth estimations (e.g., depth output 206). During runtime inferencing, the trained depth model 204 may be used to generate depth output 206 for an input subject frame 202. This depth output 206 can then be used for a variety of purposes, such as autonomous driving and/or driving assistance, as discussed above. In some aspects, at runtime, the depth model 204 may be used without consideration or use of other aspects of the training architecture 200, such as the context frame(s) 216, view synthesis function 218, pose estimation function 220, reconstructed subject frame 222, photometric loss function 224, depth gradient loss function 208, depth ground truth estimate function 210, depth supervision loss function 212, explainability mask function 214, and/or final loss function 205.” Paragraph 0050, done without context frames) and a target inference depth generated based on the target image (“Using the training architecture 200, the depth model 204 thereby learns to generate improved and more-accurate depth estimations (e.g., depth output 206). During runtime inferencing, the trained depth model 204 may be used to generate depth output 206 for an input subject frame 202. This depth output 206 can then be used for a variety of purposes, such as autonomous driving and/or driving assistance, as discussed above.” Paragraph 0050), and
wherein the dynamic mask is generated for each of the source image and the target image (“During runtime inferencing, the trained depth model 204 may be used to generate depth output 206 for an input subject frame 202.” Paragraph 0050).
With respect to claim 5, Ansari and Kentaro teach the method of claim 4. Ansari further teaches wherein the generating the dynamic mask comprises:
estimating a three-dimensional (3D) target point of a target pixel position in the target image based on:
a target pixel position of the target inference depth (“In another example, a more sophisticated model for estimating depth within the bounding polygon may be used, such as estimating the depth-per-pixel in the bounding polygon based on a 3D model of an object determined to be in the bounding polygon. For example, the 3D model could be of a type of vehicle, such as a car, small truck, large truck, SUV, tractor trailer, bus, and the like. Thus, different pixel depths may be generated with reference to a 3D model, and in some cases an estimated pose of the object based on the 3D model.” Paragraph 0042), target depth information of the target inference depth (“In another example, a more sophisticated model for estimating depth within the bounding polygon may be used, such as estimating the depth-per-pixel in the bounding polygon based on a 3D model of an object determined to be in the bounding polygon. For example, the 3D model could be of a type of vehicle, such as a car, small truck, large truck, SUV, tractor trailer, bus, and the like. Thus, different pixel depths may be generated with reference to a 3D model, and in some cases an estimated pose of the object based on the 3D model.” Paragraph 0042), and an intrinsic matrix related to an internal geometry of the sequence image (“In another example, a more sophisticated model for estimating depth within the bounding polygon may be used, such as estimating the depth-per-pixel in the bounding polygon based on a 3D model of an object determined to be in the bounding polygon. For example, the 3D model could be of a type of vehicle, such as a car, small truck, large truck, SUV, tractor trailer, bus, and the like. Thus, different pixel depths may be generated with reference to a 3D model, and in some cases an estimated pose of the object based on the 3D model.” Paragraph 0042),
applying the initial inference pose to the 3D target point to transform the 3D target point into a 3D source point of the source image (“In another example, a more sophisticated model for estimating depth within the bounding polygon may be used, such as estimating the depth-per-pixel in the bounding polygon based on a 3D model of an object determined to be in the bounding polygon. For example, the 3D model could be of a type of vehicle, such as a car, small truck, large truck, SUV, tractor trailer, bus, and the like. Thus, different pixel depths may be generated with reference to a 3D model, and in some cases an estimated pose of the object based on the 3D model.” Paragraph 0042);
determining a source pixel position corresponding to the target pixel position, wherein the source pixel position is projected at the source inference depth by applying the intrinsic matrix to the 3D source point (“In yet another example, the depth of pixels in the bounding polygon may be modeled based on distance from a central pixel (e.g., distance of pixels in bounding polygon 302 (e.g., a bounding square) from a central pixel at point 308). For example, the depth may be assumed to be related by a Gaussian function based on distance from the central pixel, or using other functions.” Paragraph 0043);
warping the source inference depth, based on a source pixel position, to the target pixel position to generate the synthetic depth (“For example, a pixel in subject frame 202 may be marked as “non- explainable” if the reprojection error for that pixel in the warped image (estimated subject frame 222) is higher than the value of the loss for the same pixel with respect to the original (unwarped) context frame 216. In this example, “warping” refers to the view synthesis operation performed by view synthesis function 218. In other words, if no associated pixel can be found with respect to the original subject frame 202 for the given pixel in the reconstructed subject frame 222, then the given pixel was probably globally non-static (or relatively static to the camera) in the subject frame 202 and therefore cannot be reasonably explained.” Paragraph 0045); and
generating, based on the synthetic depth and the target inference depth, the dynamic mask (“In another example, a more sophisticated model for estimating depth within the bounding polygon may be used, such as estimating the depth-per-pixel in the bounding polygon based on a 3D model of an object determined to be in the bounding polygon. For example, the 3D model could be of a type of vehicle, such as a car, small truck, large truck, SUV, tractor trailer, bus, and the like. Thus, different pixel depths may be generated with reference to a 3D model, and in some cases an estimated pose of the object based on the 3D model.” Paragraph 0042 and “For example, a pixel in subject frame 202 may be marked as “non- explainable” if the reprojection error for that pixel in the warped image (estimated subject frame 222) is higher than the value of the loss for the same pixel with respect to the original (unwarped) context frame 216. In this example, “warping” refers to the view synthesis operation performed by view synthesis function 218. In other words, if no associated pixel can be found with respect to the original subject frame 202 for the given pixel in the reconstructed subject frame 222, then the given pixel was probably globally non-static (or relatively static to the camera) in the subject frame 202 and therefore cannot be reasonably explained.” Paragraph 0045).
With respect to claim 6, Ansari and Kentaro teach the method of claim 4. Ansari further teaches wherein the synthetic image model is a view synthesis based self-supervised depth estimation model (“The estimated depth output 206 is also provided as an input to view synthesis function 218. View synthesis function 218 further takes as inputs one or more context frames ( I.sub.s ) 216 and a pose estimate from pose estimation function 220 and generates a reconstructed subject frame (/.sub.t) 222.” Paragraph 0035), wherein the view synthesis based self-supervised depth estimation model is
trained based on a loss function (“Reconstructed subject frame 222 may be compared against subject frame 202 by a photometric loss function 224 to generate a photometric loss, which is another component of final loss function 205. As discussed above, though not depicted in the figure, the photometric loss component may be associated with a hyperparameter (e.g., a weight) in final loss function 205, which changes the influence of the photometric loss on final loss function 205.” Paragraph 0038), and
wherein the loss function utilizes a loss based on the synthetic image and the target image to which weights of the dynamic mask are respectively applied (“Reconstructed subject frame 222 may be compared against subject frame 202 by a photometric loss function 224 to generate a photometric loss, which is another component of final loss function 205. As discussed above, though not depicted in the figure, the photometric loss component may be associated with a hyperparameter (e.g., a weight) in final loss function 205, which changes the influence of the photometric loss on final loss function 205.” Paragraph 0038).
With respect to claim 7, Ansari and Kentaro teach the method of claim 1. Kentaro further teaches wherein the generating the refined inference pose comprises:
extracting a feature of the sequence image (see figure 6A 604);
outputting, based on the dynamic mask, a feature filtered to block a dynamic region from the feature (see figure 6A 602); and
generating the refined inference pose by encoding the filtered feature (see figure 6A 610 and figure 7).
With respect to claim 8, Ansari and Kentaro teach the method of claim 7. Kentaro further teaches wherein the extracting the feature comprises generating, based on a plurality of channels having different feature characteristics, a channel aggregated feature (“The two-dimensional feature data 606 is processed, along with masked depth information 608, using pointwise encoding at 610, to generate pointwise feature data 612. The pointwise feature data 612 includes multiple feature channels for each of a set of three-dimensional points derived from the masked depth information 608.” Page 12 paragraph 4 ), and
wherein the channel has a kernel set to output a feature with the same size as the sequence image (“The two-dimensional feature data 606 is processed, along with masked depth information 608, using pointwise encoding at 610, to generate pointwise feature data 612. The pointwise feature data 612 includes multiple feature channels for each of a set of three-dimensional points derived from the masked depth information 608. The points form a point cloud representing portions of the object visible in the image. In the present example, the two-dimensional feature data 606 and the masked depth information 608 are processed separately using respective fully connected neural network layers, and the resulting pointwise features are concatenated to generate the pointwise feature data 612.” Page 12 (last paragraph)- 13 (first two lines)).
With respect to claim 9, Ansari and Kentaro teach the method of claim 7. Ansari teaches wherein the sequence image comprises a source image and a target image related to the source image in time series (“In at least one aspect, during runtime, a monocular depth model (e.g., the depth model 204) may be continuously or repeatedly used to process input frames. Intermittently, the self-supervised training architecture 200 may be activated to refine or update the depth model 204.” Paragraph 0051), wherein the dynamic mask is generated for each of the source image and the target image (“In at least one aspect, during runtime, a monocular depth model (e.g., the depth model 204) may be continuously or repeatedly used to process input frames. Intermittently, the self-supervised training architecture 200 may be activated to refine or update the depth model 204.” Paragraph 0051). Kentaro further teaches wherein the extracting the feature comprises: applying the dynamic mask to each of a feature of the source image and a feature of the target image (“The two-dimensional feature data 606 is processed, along with masked depth information 608, using pointwise encoding at 610, to generate pointwise feature data 612. The pointwise feature data 612 includes multiple feature channels for each of a set of three-dimensional points derived from the masked depth information 608. The points form a point cloud representing portions of the object visible in the image. In the present example, the two-dimensional feature data 606 and the masked depth information 608 are processed separately using respective fully connected neural network layers, and the resulting pointwise features are concatenated to generate the pointwise feature data 612.” Page 12 last paragraph- page 123 first two lines), wherein the dynamic mask corresponds to each of the feature of the source image and the feature of the target image (“The feature grid 616 is concatenated with occupancy data 620 indicating regions of the voxel grid which cannot be occupied by the target object because they are occupied by other objects or free space. The occupancy data 620 associates a binary number to each voxel of the voxel grid containing the target object, where the binary number indicates whether that voxel is impenetrable to the target object (i.e. whether the voxel is in either of states 2 or 3 referred to above). The concatenated feature grid 616 and occupancy data 620 therefore include, for each voxel of the voxel grid, a binary channel from the occupancy data 620 and multiple channels from the feature grid 616. The concatenated feature grid 616 and occupancy data 620 therefore contain information derived from the masked image data 602 and masked point cloud 610, and further contains information depending on the objects and space surrounding the target object.” Page 13 paragraph 2 ); and
outputting each filtered feature corresponding to each of the source image and the target image (see figure 6A 602), and wherein the generating the refined inference pose comprises concatenating each filtered feature (“The feature grid 616 is concatenated with occupancy data 620 indicating regions of the voxel grid which cannot be occupied by the target object because they are occupied by other objects or free space. The occupancy data 620 associates a binary number to each voxel of the voxel grid containing the target object, where the binary number indicates whether that voxel is impenetrable to the target object (i.e. whether the voxel is in either of states 2 or 3 referred to above). The concatenated feature grid 616 and occupancy data 620 therefore include, for each voxel of the voxel grid, a binary channel from the occupancy data 620 and multiple channels from the feature grid 616. The concatenated feature grid 616 and occupancy data 620 therefore contain information derived from the masked image data 602 and masked point cloud 610, and further contains information depending on the objects and space surrounding the target object.” Page 13 paragraph 2 ) and encoding the concatenated features to generate the refined inference pose (see figure 6A 610 and figure 7).
With respect to claim 10, Ansari and Kentaro teach the method of claim 1. Kentaro teaches the refined inference pose (“Having determined an estimate for the pose of each of the plurality of objects, and transformed the estimated poses to a common reference frame if necessary, the system 100 performs an iterative pose refinement routine to jointly optimise the estimated poses as described hereafter. The pose refinement routine starts at 706, where the system 100 samples a set of points from a stored model of each given object, transformed in accordance with the corresponding estimated pose of the object. In this example, the stored model is a volumetric solid model and the system 100 samples the set of points uniformly from throughout the volume of the volumetric solid model.” Page 18 (last 3 lines- 19 (lines1-5)) and Ansari teaches the method of claim further comprising:
generating, based on a synthetic depth, a subsequent dynamic mask, wherein the synthetic depth is generated based on the inference depth and the inference pose (“In at least one aspect, during runtime, a monocular depth model (e.g., the depth model 204) may be continuously or repeatedly used to process input frames. Intermittently, the self-supervised training architecture 200 may be activated to refine or update the depth model 204.” Paragraph 0051);
generating the inference depth to replace the dynamic mask (“In at least one aspect, during runtime, a monocular depth model (e.g., the depth model 204) may be continuously or repeatedly used to process input frames. Intermittently, the self-supervised training architecture 200 may be activated to refine or update the depth model 204.” Paragraph 0051); and
generating, based on the sequence image and the subsequent dynamic mask, a subsequent
refined inference pose to replace the refined inference pose (“In at least one aspect, during runtime, a monocular depth model (e.g., the depth model 204) may be continuously or repeatedly used to process input frames. Intermittently, the self-supervised training architecture 200 may be activated to refine or update the depth model 204.” Paragraph 0051).
With respect to claim 11, Ansari and Kentaro teach all limitations in consideration of claim 1, due to the substantial similarity between the claims, with claim 11 being directed to an apparatus capable of performing the method of claim 1. Ansari further teaches a processor (“A processing system comprising: a memory comprising computer- executable instructions; and one or more processors configured to execute the computer- executable instructions and cause the processing system to perform a method in accordance with any of Clauses 1-35.” Paragraph 0147) and memory configured to store at least one instruction (“A processing system comprising: a memory comprising computer- executable instructions; and one or more processors configured to execute the computer- executable instructions and cause the processing system to perform a method in accordance with any of Clauses 1-35.” Paragraph 0147).
With respect to claim 12, Ansari and Kentaro teach the apparatus of claim 11 and all limitations in consideration of claim 2, due to the substantial similarity between the claims, with claim 12 being directed to an apparatus capable of performing the method of claim 2.
With respect to claim 13, Ansari and Kentaro teach the apparatus of claim 11 and all limitations in consideration of claim 3, due to the substantial similarity between the claims, with claim 13 being directed to an apparatus capable of performing the method of claim 3.
With respect to claim 14, Ansari and Kentaro teach the apparatus of claim 11 and all limitations in consideration of claim 4, due to the substantial similarity between the claims, with claim 14 being directed to an apparatus capable of performing the method of claim 4.
With respect to claim 15, Ansari and Kentaro teach the apparatus of claim 14 and all limitations in consideration of claim 5, due to the substantial similarity between the claims, with claim 15 being directed to an apparatus capable of performing the method of claim 5.
With respect to claim 16, Ansari and Kentaro teach the apparatus of claim 14 and all limitations in consideration of claim 6, due to the substantial similarity between the claims, with claim 16 being directed to an apparatus capable of performing the method of claim 6.
With respect to claim 17, Ansari and Kentaro teach the apparatus of claim 11 and all limitations in consideration of claim 7, due to the substantial similarity between the claims, with claim 17 being directed to an apparatus capable of performing the method of claim 7.
With respect to claim 18, Ansari and Kentaro teach the apparatus of claim 17 and all limitations in consideration of claim 8, due to the substantial similarity between the claims, with claim 18 being directed to an apparatus capable of performing the method of claim 8.
With respect to claim 19, Ansari and Kentaro teach the apparatus of claim 17 and all limitations in consideration of claim 9, due to the substantial similarity between the claims, with claim 19 being directed to an apparatus capable of performing the method of claim 9.
With respect to claim 20, Ansari and Kentaro teach the apparatus of claim 11 and all limitations in consideration of claim 10, due to the substantial similarity between the claims, with claim 20 being directed to an apparatus capable of performing the method of claim 10.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ruhkamp (CN 114916239 A)- discloses pose change estimation based on depth values
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew W 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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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
