DETAILED ACTION
Status of Claims
Applicant's amendments filed on 10 July 2026 have been entered. Claims 1, 14, and 15 have been amended. No claims have been canceled. No claims have been added. Claims 1-15 are still pending in this application, with claims 1, 14, and 15 being independent.
Response to Arguments
Applicant’s arguments with respect to claims 1-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Tagra et al. (US Pub. 2022/0121839), hereinafter Tagra, in view of Sinha et al. (US Pub. 2023/0094954), hereinafter Sinha.
Regarding claim 1, Tagra discloses a data creation method performed by a data creation device, the data creation method comprising: by partially or wholly changing a creation parameter which is obtained by conversion of a source image of a freely-selected face to a numerical value, creating a larger number of input creation parameters than a predetermined number from the predetermined number of creation parameters of the source images (Fig. 4A; Fig. 5; Paragraph [0055]: Process 400 includes, at preliminary stage 410, resizing an input image 412. Resizing may include rescaling input image 412 into different resolutions to form an image pyramid 414, which is a set of images corresponding to the input image 412 but having different resolutions. Image pyramid 414 is input into MTCNN such that each image of image pyramid 414 is run through the three network stages of MTCNN. A first stage 420 comprises a proposal network (P-Net), which is a shallow convolutional neural network (CNN) that identifies candidates for the most likely facial regions within the input image 412. Candidates may be calibrated using estimated bounding box regression, and certain overlapped candidates may be merged using non-maximum suppression (NMS). Candidates identified in the first stage 420 are input into a second stage 430, which comprises a refine network (R-Net). R-Net is a CNN that filters out the false candidates. The second stage 430 may also include NMS and bounding box regression to calibrate the determined bounding boxes. A third stage 440 comprises an output network (O-Net), which is a CNN that detects particular details within the facial region candidates remaining after the second stage 430. In exemplary aspects, O-Net outputs a final bounding box 442 for a face and select facial landmarks 444. The facial landmarks output by O-Net are landmarks for the eyes, nose and mouth; Paragraph [0066]: a set of similar facial images are selected from the reference facial images, where the selected facial images are the top n reference facial images that have the most similarity with the base facial image. For embodiments utilizing Euclidean distance, the reference facial images with the smallest distance are selected as most similar to the base facial images. In some embodiments, the number of images selected for the set of similar images (n) may be a value within a range of two and ten. For example, the set of similar facial images may consist of six reference facial images that have been determined to be most similar to the base facial image. FIG. 5 depicts an example set of similar facial images 510 that may be identified for the example base facial image 512); by creating a face image data item on a basis of a plurality of the input creation parameters, creating a face image dataset including a plurality of the face image data items (Fig. 5; Fig. 7; Paragraphs [0071]-[0073]: some embodiments of new facial synthesizer 220 further perform triangulation by dividing each similar facial image and the output image space (where the landmark coordinates are the averaged coordinates) into triangular regions. The triangular regions are determined from the detected facial landmarks. In exemplary embodiments, at least one point on each triangular region is the coordinate of a facial landmark. Further, in exemplary embodiments, Delaunay Triangulation is performed, where a convex hull (a boundary that has no concavities) is created and the triangulation that is chosen is the one in which every circumcircle of a triangle is an empty circle. The triangular regions in each similar facial image are warped to the triangular regions in the output image space using affine transformations. Triangulation helps ensure that similar facial regions in the similar facial images fall in the same region of the output space so that they can be combined as described below. FIG. 6C depicts warped similar image 630 that is warped to the output image space using triangulation. FIG. 7 shows the results of this landmark registration and warping process. Specifically, FIG. 7 depicts the example set of similar facial images 510 of FIG. 5 and a set of warped similar facial images 710 that are all warped to the same output image space …After the similar facial images are aligned and warped to an output image space, the similar facial images are combined by averaging pixel intensities of each similar facial image at each pixel within the output image space. In some embodiments, an average pixel intensity for the output image space is computed by summing the corresponding pixels values of all the warped similar facial images and dividing the sum by the number of similar facial images within the set. In some embodiments, a weighted average may be computed. This result of averaging similar facial images is a newly synthesized facial image. Because this new facial image depicts a face that is created from a combination imaged faces, it does not depict an actual person's face. In this way, the new facial image created by new facial image synthesizer 220 does not expose another person's identity. FIG. 8 depicts an example new facial image 800 that is from the example set of similar facial images 510 of FIG. 5…After a new facial image is created from similar facial images, it is combined with the base facial image. As such, new user facial image generator 230 is generally responsible for combining the base facial image and the new facial image. The resulting combination may be referred to herein as a new user facial image; however, it is should be understood that, in some implementations, this combination is based on a base facial image selected from a database rather than a user input facial image. Combining the new facial image with the base facial image helps to preserve some aesthetics, such as complexion, structure, and size, of the base facial image).
Tagra does not explicitly disclose cleansing the plurality of the face image data items to remove an attribute bias to cause a distribution of attribute values of the face image dataset to match a target value.
However, Sinha teaches synthesis of facial images (Paragraphs [0093]-[0099]), further comprising cleansing the plurality of the face image data items to remove an attribute bias to cause a distribution of attribute values of the face image dataset to match a target value (Fig. 1; Paragraph [0039]: FIG. 1 shows a schematic diagram 100 of a technique for generating simulated images that enhance socio-demographic diversity, according to some embodiments. The schematic diagram 100 includes a query 102 submitted by a user device to a multimedia database 104, in order to retrieve a set of images relating to a particular occupation (e.g., a contractor). The multimedia database server 104 accesses, from an image repository (not shown) storing a plurality of images across various occupations, a set of existing images 106 that correspond to a contractor. As explained above, the set of existing images 106 can be biased towards a particular socio-demographic stereotype. To reduce such bias and enhance socio-demographic diversity, a set of simulated images 108 can be added to the multimedia database; Paragraph [0117]: although the distribution of different attributes in the dataset is not uniform, oversampling from the dataset can be performed to create a uniform distribution during training and thereby reduce bias in the training dataset. In some instances, the oversampling includes generating duplicated copies of training images that corresponding to the set of target socio-demographic attributes. This will lead to better mapping of combinations of non-stereotypical attributes. However, oversampling alone may lead to an overfitting issue, as the same images from rare classes will appear more times and can destabilize the training. For example, the FID score drops initially. After a certain stage, however, the FID score starts to increase continuously, and training begins to diverge). Sinha teaches that this will allow for the training dataset to reduce any imbalance (Paragraph [0036]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tagra with the features of above as taught by Sinha so as to reduce any imbalance as presented by Sinha.
Regarding claim 2, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein the data creation device creates a plurality of the input creation parameters from one creation parameter by increasing or reducing some or all of a plurality of parameters constituting the creation parameter (Fig. 4A; Paragraph [0056]: the bounding box output from MTCNN is an initial bounding box, and input face detector 210 applies padding around the initial bounding box coordinates to enlarge the bounding box enclosing the face of the input image. In exemplary embodiments, the amount added to the height of the bounding box is the same amount added to the width. For example, padding may include adding five percent of the initial bounding box height and five percent of the initial bounding box width. It is contemplated that other values may be used for padding the bounding box. Utilizing the final bounding box for an input image, a base facial image may be extracted from the input image for further processing by one or more components of identity obfuscation manager 200).
Regarding claim 3, Tagra, in view of Sinha teaches the data creation method according to claim 1, wherein, Tagra discloses by changing a specific parameter constituting the creation parameter, the data creation device creates the input creation parameters (Fig. 2; Paragraphs [0077]-[0079]: Input image updater 240 is generally responsible for updating the input image with the new user facial image generated by new user facial image generator 230. As the input image may often have other aspects of the individual, such as hair and body, that are not part of the generated new user facial image, input image updater 240 operates to help create a seamless blend between the new user facial image and the input image…aspects in which the base facial image is extracted from the input image, input image updater 240 aligns the new user facial image with the face detected in the input image because the face from the input image indicates a natural-appearing alignment of a face with the rest of the input image. This process may be similar to the alignment and morphing process described with respect to the similar facial images. Facial landmarks are extracted and used to form a convex hull over the detected facial landmarks, and triangulation is performed inside the convex hull, using the facial landmark coordinates. In some embodiments, the triangular regions have points corresponding to angles or midpoints along the convex hull, rather than along a facial image boundary box as described in some embodiments of new facial image synthesizer 220. The triangular regions of the convex hull of the new user facial image are warped to the face within the input image…aligning the new user facial image within the input image, embodiments of input image updater 240 blend the new user facial image with the background of the input image. In exemplary embodiments, input image updater 240 uses Poisson blending, which utilizes image gradients such that the gradient of the facial region within the resulting updated input image is the same or almost the same as the gradient of the facial region in the input image. Additionally, the intensity of intensity of the new user facial image may be adjusted so that it is the same as the intensity of the original facial region detected within the input image; Paragraph [0094]: new user facial image generated for a faceless image is added to the input image by input image updater 240. This process includes aligning the new user facial image within the input image and blending the two images together. Because the input image is faceless in this context, input image updater 240 aligns the new user facial image within the input image in a different manner than that previously described for input images having faces. Where the input image is faceless and the new user facial image is created from a selected base facial image, input image updater 240 separates the face from the background in the new user facial image utilizing a foreground and background separation technique).
Regarding claim 4, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein, by blending the input creation parameters and the creation parameter of a preset face image prepared in advance, the data creation device creates final input creation parameters (Paragraphs [0077]-[0079]: Input image updater 240 is generally responsible for updating the input image with the new user facial image generated by new user facial image generator 230. As the input image may often have other aspects of the individual, such as hair and body, that are not part of the generated new user facial image, input image updater 240 operates to help create a seamless blend between the new user facial image and the input image…aspects in which the base facial image is extracted from the input image, input image updater 240 aligns the new user facial image with the face detected in the input image because the face from the input image indicates a natural-appearing alignment of a face with the rest of the input image. This process may be similar to the alignment and morphing process described with respect to the similar facial images. Facial landmarks are extracted and used to form a convex hull over the detected facial landmarks, and triangulation is performed inside the convex hull, using the facial landmark coordinates. In some embodiments, the triangular regions have points corresponding to angles or midpoints along the convex hull, rather than along a facial image boundary box as described in some embodiments of new facial image synthesizer 220. The triangular regions of the convex hull of the new user facial image are warped to the face within the input image…aligning the new user facial image within the input image, embodiments of input image updater 240 blend the new user facial image with the background of the input image. In exemplary embodiments, input image updater 240 uses Poisson blending, which utilizes image gradients such that the gradient of the facial region within the resulting updated input image is the same or almost the same as the gradient of the facial region in the input image. Additionally, the intensity of intensity of the new user facial image may be adjusted so that it is the same as the intensity of the original facial region detected within the input image).
Regarding claim 5, Tagra, in view of Sinha teaches the data creation method according to claim 4, Tagra discloses wherein the preset face image includes an imaginary face image (Fig. 2; Paragraph [0053]: Referring to FIG. 2, aspects of an illustrative identity obfuscation manager 200 are shown, in accordance with various embodiments of the present disclosure. At a high level, identity obfuscation manager, which may be implemented within an operating environment as described with respect to identity obfuscation manager 106 in environment 100, facilitates operations for obfuscating facial identity in a user image by synthesizing a new face (embodied as a new facial image) to be combined with the user's input image. Embodiments of identity obfuscation manager 200 includes input face detector 210, new facial image synthesizer 220, new user facial image generator 230, input image updater 240, and data store 250).
Regarding claim 6, Tagra, in view of Sinha teaches the data creation method according to claim 4, Tagra discloses wherein the data creation device performs cleansing on the creation parameters of a plurality of the preset face images, and blends the creation parameters of the preset face images having undergone the cleansing and the input creation parameters (Fig. 2; Paragraphs [0077]-[0079]: Input image updater 240 is generally responsible for updating the input image with the new user facial image generated by new user facial image generator 230. As the input image may often have other aspects of the individual, such as hair and body, that are not part of the generated new user facial image, input image updater 240 operates to help create a seamless blend between the new user facial image and the input image…aspects in which the base facial image is extracted from the input image, input image updater 240 aligns the new user facial image with the face detected in the input image because the face from the input image indicates a natural-appearing alignment of a face with the rest of the input image. This process may be similar to the alignment and morphing process described with respect to the similar facial images. Facial landmarks are extracted and used to form a convex hull over the detected facial landmarks, and triangulation is performed inside the convex hull, using the facial landmark coordinates. In some embodiments, the triangular regions have points corresponding to angles or midpoints along the convex hull, rather than along a facial image boundary box as described in some embodiments of new facial image synthesizer 220. The triangular regions of the convex hull of the new user facial image are warped to the face within the input image…aligning the new user facial image within the input image, embodiments of input image updater 240 blend the new user facial image with the background of the input image. In exemplary embodiments, input image updater 240 uses Poisson blending, which utilizes image gradients such that the gradient of the facial region within the resulting updated input image is the same or almost the same as the gradient of the facial region in the input image. Additionally, the intensity of intensity of the new user facial image may be adjusted so that it is the same as the intensity of the original facial region detected within the input image; Paragraph [0094]: new user facial image generated for a faceless image is added to the input image by input image updater 240. This process includes aligning the new user facial image within the input image and blending the two images together. Because the input image is faceless in this context, input image updater 240 aligns the new user facial image within the input image in a different manner than that previously described for input images having faces. Where the input image is faceless and the new user facial image is created from a selected base facial image, input image updater 240 separates the face from the background in the new user facial image utilizing a foreground and background separation technique).
Regarding claim 7, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein the data creation device scrambles the creation parameter of the source image, and creates the input creation parameters on a basis of the scrambled creation parameter (Paragraph [0040]: embodiments of the present invention are directed to facilitating realistic and aesthetically pleasing facial identity obfuscation. At a high level, when an input image is received from or by the direction of a user, a base face for the input image is determined. Face detection technologies are applied to determine if the input image depicts a face, which will be used as the base face. If not, such as when the face is cropped out of the image, is blurred, or is otherwise obscured, the base face is selected from reference facial images. Based on the base face for an input image, a set of similar facial images are selected and used to synthesize a new facial image. The new facial image is combined with the base face, and the input image is updated with the combination of the new facial image and base face, which helps to retain some aesthetics of the input image).
Regarding claim 8, Tagra, in view of Sinha teaches the data creation method according to claim 7, Tagra discloses wherein the data creation device performs the scrambling by adding a random noise to a parameter in a specific layer of a plurality of layers constituting the creation parameter of the source image (Fig. 4B; Paragraphs [0057]-[0060]: FIG. 4B depicts a network architecture for an embodiment of input face detector 210. Specifically, FIG. 4B depicts an example MTCNN architecture 450, which includes a P-Net 452, an R-Net 454, and an O-Net 456. Each network (also referred to as a stage) includes a series of max pooling (designated as “MP” in FIG. 4B) and convolutional (designated as “cony” in FIG. 4B) layers. In one embodiment, the step size in convolution is one, and the step size in pooling two…input face detector 210 detects a face depicted in the input image, the face extracted from the input image may be referred to as a base facial image that is utilized for synthesis of the new facial image as described herein. In some instances, however, input face detector 210 may not detect a face in the input image, such as where the face is cropped out of the input image, is blurred, or is otherwise obscured by an object, such as a block box, in the input image. In some embodiments, if input face detector 210 is unable to detect a face, a message is displayed to the user requesting that a new image depicting a face be input. Additionally, the message may indicate that a new face will be generated for the input image to help protect the user's identity. Where a user does not upload an image with a detectable face (such as where the user initially inputs an image without a face or refuses to input a new image upon receiving a request), some embodiments of the disclosed technology select a base facial image for the input image as described further below with respect to base face selector 270).
Regarding claim 9, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein, by performing face ID/attribute labeling and cleansing on the face image dataset, the data creation device creates a final face image dataset (Fig. 10E; Paragraph [0080]: FIGS. 10A-10E depict a series of images as user image is updated with a new user facial image. FIG. 10A depicts a new user facial image 1010 with detected facial landmarks 1012 in a convex hull 1014. FIG. 10B depicts a triangulated new user facial image 1020 that is divided into triangles based on the facial landmarks 1012 and convex hull 1014. FIG. 10C depicts a portion of an aligned updated input image 1030 in which the new user facial image 1010 is aligned with the face region of the user input image. FIG. 10D depicts a portion of blended updated input image 1040 in which the new user facial image and the rest of the updated input image are blended together to create a seamless transition between the original aspects of the input image and the new user facial image that has been created. FIG. 10E depicts the final updated input image 1050 in its entirety).
Regarding claim 10, Tagra, in view of Sinha teaches the data creation method according to claim 9, Tagra discloses wherein, by performing cleansing on the face image dataset, the data creation device creates the final face image dataset that satisfies a predetermined requirement (Paragraph [0097]: identity obfuscation manager 200 include a similarity score determiner 280 that is generally responsible for determining a similarity score for an updated input image. A similarity score is a metric measuring the degree of similarity or the degree of difference between the updated input image and the original input image. In some embodiments, the similarity score is utilized to ensure that some aesthetics of the input image are retained in the updated user input image. In these cases, the similarity score determined for an updated input image is compared to a predetermined minimum threshold similarity score, and if the similarity score for the updated input image does not meet the minimum threshold, a user is provided a notification indicating that identity obfuscation will change the aesthetics of the user's input image. Additionally or alternatively, the similarity score may be utilized to ensure that the updated input image is not too similar to the input image so that the identity is not properly protected. As such, in these instances, the similarity score determined for an updated input image is compared to a predetermined maximum threshold similarity score, and if the similarity score for the updated input image does not meet the maximum threshold, a user is provided a notification indicating that facial identity could not be protected. Where the updated input image does not meet the maximum threshold score, the updated input image may either be presented with the notification or may not be presented at all. In some embodiment, the determined similarity score is compared to both a maximum score and a minimum score to determine whether the updated input image both sufficiently obfuscates the individual's identity and preserves the aesthetics of the original input image. Further, in some embodiments, the similarity score is computed and provided as feedback to other components of identity obfuscation manager 200 to help improve the functioning of identity obfuscation manager 200).
Regarding claim 11, Tagra, in view of Sinha teaches the data creation method according to claim 10, Tagra discloses wherein the predetermined requirement is the number of the face image data items constituting the face image dataset, a resolution of the face image data items, an attribute to be subjected to labeling, or statistics of attribute values of attributes of the face image data items (Paragraphs [0097]-[0098]: identity obfuscation manager 200 include a similarity score determiner 280 that is generally responsible for determining a similarity score for an updated input image. A similarity score is a metric measuring the degree of similarity or the degree of difference between the updated input image and the original input image. In some embodiments, the similarity score is utilized to ensure that some aesthetics of the input image are retained in the updated user input image. In these cases, the similarity score determined for an updated input image is compared to a predetermined minimum threshold similarity score, and if the similarity score for the updated input image does not meet the minimum threshold, a user is provided a notification indicating that identity obfuscation will change the aesthetics of the user's input image. Additionally or alternatively, the similarity score may be utilized to ensure that the updated input image is not too similar to the input image so that the identity is not properly protected. As such, in these instances, the similarity score determined for an updated input image is compared to a predetermined maximum threshold similarity score, and if the similarity score for the updated input image does not meet the maximum threshold, a user is provided a notification indicating that facial identity could not be protected. Where the updated input image does not meet the maximum threshold score, the updated input image may either be presented with the notification or may not be presented at all. In some embodiment, the determined similarity score is compared to both a maximum score and a minimum score to determine whether the updated input image both sufficiently obfuscates the individual's identity and preserves the aesthetics of the original input image. Further, in some embodiments, the similarity score is computed and provided as feedback to other components of identity obfuscation manager 200 to help improve the functioning of identity obfuscation manager 200…Exemplary embodiments of similarity score determiner 280 determines the similarity score by computing a structural similarity (SSIM) index value. SSIM index is a method in image processing used for predicting the perceived quality of an image. The SSIM index value is calculated on various windows of an image. The measure between two windows x and y of common size (N×N) is determined).
Regarding claim 12, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein, by converting the source image to a numerical value, the data creation device creates the creation parameter according to the source image (Fig. 4A; Fig. 5; Paragraph [0055]: Process 400 includes, at preliminary stage 410, resizing an input image 412. Resizing may include rescaling input image 412 into different resolutions to form an image pyramid 414, which is a set of images corresponding to the input image 412 but having different resolutions. Image pyramid 414 is input into MTCNN such that each image of image pyramid 414 is run through the three network stages of MTCNN. A first stage 420 comprises a proposal network (P-Net), which is a shallow convolutional neural network (CNN) that identifies candidates for the most likely facial regions within the input image 412. Candidates may be calibrated using estimated bounding box regression, and certain overlapped candidates may be merged using non-maximum suppression (NMS). Candidates identified in the first stage 420 are input into a second stage 430, which comprises a refine network (R-Net). R-Net is a CNN that filters out the false candidates. The second stage 430 may also include NMS and bounding box regression to calibrate the determined bounding boxes. A third stage 440 comprises an output network (O-Net), which is a CNN that detects particular details within the facial region candidates remaining after the second stage 430. In exemplary aspects, O-Net outputs a final bounding box 442 for a face and select facial landmarks 444. The facial landmarks output by O-Net are landmarks for the eyes, nose and mouth; Paragraph [0066]: a set of similar facial images are selected from the reference facial images, where the selected facial images are the top n reference facial images that have the most similarity with the base facial image. For embodiments utilizing Euclidean distance, the reference facial images with the smallest distance are selected as most similar to the base facial images. In some embodiments, the number of images selected for the set of similar images (n) may be a value within a range of two and ten. For example, the set of similar facial images may consist of six reference facial images that have been determined to be most similar to the base facial image. FIG. 5 depicts an example set of similar facial images 510 that may be identified for the example base facial image 512).
Regarding claim 13, Tagra, in view of Sinha teaches the data creation method according to claim 1, Tagra discloses wherein the data creation device creates the face image data items by using a GAN or a VAE according to the input creation parameters (Paragraph [0039]: Some existing techniques for protecting facial identity involve superimposing a stock image of a person's face over a user's uploaded image to change the face in the user uploaded image. While this technique may protect the user's identity, it does so at the expense of the privacy of the actual individual in the other image used. Some existing methods utilizing Generative Adversarial Networks (GANs) synthesize a new face instead of using a stock image).
Regarding claim 14, the limitations of this claim substantially correspond to the limitations of claim 1; thus they are rejected on similar grounds.
Regarding claim 15, the limitations of this claim substantially correspond to the limitations of claim 1; thus they are rejected on similar grounds.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D SALVUCCI whose telephone number is (571)270-5748. The examiner can normally be reached M-F: 7:30-4:00PT.
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/MATTHEW SALVUCCI/Primary Examiner, Art Unit 2613
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