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 .
In view of the amendment dated 12/23/2025 to claims 25-31, the 35 USC 101 rejection is withdrawn.
Applicant's arguments directed to the 35 USC 112(a) and 35 USC 112(b) rejections of claims 21 and 29 have been fully considered but they are not persuasive. The applicant argues on pages 9-10 that the disclosure at paragraphs 0063, 0086 and 0104-0108 support the claimed limitations. The Examiner respectfully disagrees. The referenced paragraphs discuss extracting data items according to some metric, e.g., selected randomly or according to a determined set of classes. Although this extraction may be accomplished through a statistical method, this is not inherent. Other means of accomplishing the claimed extraction could be utilized. Dependent on a user’s choice, the resulting extracted data could result in a different data set. This can certainly affect any resulting processes. Because the applicant has not disclosed any statistical methods that may be used or even stated the extraction is accomplished using such, the claim is not supported by the disclosure. The 35 USC 112(a) and 35 USC 112(b) rejections are maintained.
Applicant's arguments directed to the 35 USC 102 rejection of claims 1-7, 9-23 and 25-31 have been fully considered but they are not persuasive. The applicant argues on pages 11-12 that applied reference to El-Khamy does not teach “… identify one or more features of an object based, at least in part, on a label of the object.” The Examiner respectfully disagrees. El-Khamy discloses at paragraphs 0043-0045 discloses utilizing “labeled training data” in the disclosed training process (previously cited in claim 31). Because the semantic segmentation generates for each object in the image a separate mask identifying the relevant pixels corresponding to each object instance, El-Khamy appears to fully disclose the presently amended claims. Applicant’s argument directed to dependent claims 2-7, 10-16, 18-24 and 26-31 are based on the arguments directed to the respective independent claims which are not persuasive. Therefore, the dependent claims are likewise discussed.
Applicant's arguments directed to the 35 USC 103 rejection of claims 8, 24 and 28 have been fully considered but they are not persuasive. The applicant argues on pages 12-13 the claimed limitation in view of the preceding arguments directed at their respective independent claim. Because the arguments directed to the independent claims are not persuasive, neither are arguments directed to claims 8, 24 and 28. Therefore, the dependent claims are likewise discussed.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21 and 29 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 21 recites “… wherein the one or more images of the object is a subset of a set of images, the subset to be selected using statistical methods.” The disclosure does not discuss any statistical method by which a subset is selected or determined. Although the disclosure states the above limitation in the originally filed claim language, there is no apparent further disclosure what statistical method is employed or envisioned by the inventors. There are a vast number of statistical methods; without inventor disclosure, the intent of the inventors is unknown.
Claim 29 is rejected similarly to claim 21 above.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21 and 29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 recites “… wherein the one or more images of the object is a subset of a set of images, the subset to be selected using statistical methods.” It is indefinite as to the metes and bounds of this limitation. What statistical method is applied such that an object is a subset of images? What type or category of statistical method should be applied as envisioned by the inventor? What is the criteria to fall within a subset?
Claim 29 is rejected similarly to claim 21 above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-23 and 25-31 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by El-Khamy et al, (US Pub No. 20190057507).
Claim 1: El-Khamy discloses one or more processors comprising:
circuitry to use one or more neural networks [core instance feature extraction network may include a fully convolutional instance semantic segmentation network, p0019] to:
identify one or more features of an object based, at least in part, on a label of the object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image … the present disclosure may be trained using labeled training data (e.g., using a supervised learning process), p0043-0045];
use the identified one or more features of the object to generate one or more images depicting the object [a multiscale resolution feature map calculator configured to calculate a plurality of feature maps at multiscale resolutions from the core instance features … a semantic segmentation system may generate, for each separate instance of an object in the image (e.g., each instance of a car in the scene), a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object, p0018 & p0044]; and
use the identified one or more features of the object to generate one or more segmentation masks indicating which pixels depict the object in the generated one or more images [a segmentation mask prediction network configured to calculate a plurality of segmentation masks for each detection box of the detection boxes at the multiscale resolutions of the feature maps … a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object. For example, if the semantic segmentation system detects three cars and two pedestrians in the image, five separate instance masks are output: one for each of the cars and one for each of the pedestrians, p0018 & p0044].
Claim 2: El-Khamy discloses the one or more processors of claim 1, wherein the one or more networks include at least a second neural network to generate the one or more segmentation masks based [a segmentation mask prediction network configured to calculate a plurality of segmentation masks, p0018], at least in part, on intermediate feature information provided by one or more first neural networks of the one or more neural networks [feature pyramid network may be configured to generate a feature map of the plurality of feature maps by: upsampling the core instance features from the fully convolutional semantic segmentation network; applying a convolutional kernel to a previous feature map to generate a convolved previous feature map … the later stage feature maps (e.g., the third and fifth feature maps 230 and 250) have a larger receptive field and also have higher resolutions than earlier stage feature maps, p0021 & p0056-0057].
Claim 3: El-Khamy discloses the one or more processors of claim 1, wherein one or more first neural networks generate the one or more images of the object based, at least in part on segmentation information generated by one or more second neural networks [a pyramid segmentation network configured to merge the plurality of segmentation masks at the multiscale resolutions to generate an instance mask for each object detected in the input image, p0018 & p0020].
Claim 4: El-Khamy discloses the one or more processors of claim 1, wherein the one or more images of the object are to be synthesized by one of the one or more neural networks [an output module configured to output the instance masks as the detected instances of the objects in the input image, p0018 & p0044].
Claim 5: El-Khamy discloses the one or more processors of claim 1, wherein the identified one or more features of the object include class labels of the object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image, p0043-0046].
Claim 6: El-Khamy discloses the one or more processors of claim 1, wherein the one or more segmentation masks include information identifying one or more pixels of the one or more images as part of a determined class [e.g., semantic segmentation of an image of a street scene may label all of the pixels associated with each car in the scene with the label of “car,” all of the pixels associated with a person on a bicycle with the label “bicycle,” and may label all of the pixels associated with people walking in the scene with the label of “pedestrian.” Furthermore, a semantic segmentation system may generate, for each separate instance of an object in the image (e.g., each instance of a car in the scene), a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object, p0044].
Claim 7: El-Khamy discloses the one or more processors of claim 1, wherein the one or more neural networks are to be trained based, at least in part, on the one or more segmentation masks [segmentation mask head is a fully convolutional deep neural network that is trained to predict a segmentation mask for each box proposal 302 from the RPN 300, and for each object class, p0056].
Claim 9: El-Khamy discloses a computer-implemented method comprising: using one or more neural networks [core instance feature extraction network may include a fully convolutional instance semantic segmentation network, p0019] to:
identify one or more features of an object based, at least in part, on a label of the object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image … the present disclosure may be trained using labeled training data (e.g., using a supervised learning process), p0043-0045];
use the identified one or more features of the object to generate one or more images depicting the object [a multiscale resolution feature map calculator configured to calculate a plurality of feature maps at multiscale resolutions from the core instance features … a semantic segmentation system may generate, for each separate instance of an object in the image (e.g., each instance of a car in the scene), a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object, p0018 & p0044]; and
use the identified one or more features of the object to generate one or more segmentation masks indicating which pixels depict the object in the generated one or more images [a segmentation mask prediction network configured to calculate a plurality of segmentation masks for each detection box of the detection boxes at the multiscale resolutions of the feature maps … a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object. For example, if the semantic segmentation system detects three cars and two pedestrians in the image, five separate instance masks are output: one for each of the cars and one for each of the pedestrians, p0018 & p0044].
Claim 10: El-Khamy discloses the computer-implemented method of claim 9, wherein the one or more neural networks include at least a second neural network to generate the segmentation mask information based [a segmentation mask prediction network configured to calculate a plurality of segmentation masks, p0018], at least in part, on feature information output, to be received as an input, from a first neural network of the one or more neural networks [feature pyramid network may be configured to generate a feature map of the plurality of feature maps by: upsampling the core instance features from the fully convolutional semantic segmentation network; applying a convolutional kernel to a previous feature map to generate a convolved previous feature map, p0021].
Claim 11: El-Khamy discloses the computer-implemented method of claim 9, wherein: a first neural networks architecture that is different than a second neural networks architecture [e.g., a fully convolutional instance semantic segmentation network and a segmentation mask prediction network, p0019-0021].
Claim 12: El-Khamy discloses the computer-implemented method of claim 9, further comprising: training the one or more neural networks based, at least in part, on the segmentation mask information [segmentation mask head is a fully convolutional deep neural network that is trained to predict a segmentation mask for each box proposal 302 from the RPN 300, and for each object class, p0056].
Claim 13: El-Khamy discloses the computer-implemented method of claim 9, further comprising: providing high-level features generated by a first neural network of the one or more neural networks to a second neural network [e.g., a core instance feature extraction network configured to generate a plurality of core instance features from the input image; a multiscale resolution feature map calculator configured to calculate a plurality of feature maps at multiscale resolutions from the core instance features, p0018-0020].
Claim 14: El-Khamy discloses the computer-implemented method of claim 9, further comprising: providing mid-level features generated by a first neural network to [the instance semantic segmentation system 10 provides segmentation mask prediction by aggregating all intermediate feature maps of different scales, p0056-0057].
Claim 15: El-Khamy discloses the computer-implemented method of claim 9, further comprising: providing low-level features generated by the first neural network to the second neural network [segmentation mask head is a fully convolutional deep neural network that is trained to predict a segmentation mask for each box proposal 302 from the RPN 300, and for each object class … features at three scales are shown as 410, 430, and 450, which represent feature maps at the different feature scales that are from different feature maps 210, 230, and 250 computed by the FPN 200 … the later stage feature maps (e.g., the third and fifth feature maps 230 and 250) have a larger receptive field and also have higher resolutions than earlier stage feature maps, p0056-0057].
Claim 16: El-Khamy discloses the computer-implemented method of claim 9, wherein the one or more images are synthesized by the one or more neural networks, based at least in part, on one or more features of the object [an output module configured to output the instance masks as the detected instances of the objects in the input image, p0018 & p0044].
Claim 17: El-Khamy discloses a computer system comprising:
one or more processors and memory storing executable instructions that, if performed by the one or more processors, use one or more neural networks [core instance feature extraction network may include a fully convolutional instance semantic segmentation network, p0019 & p0097] to identify one or more features of an object based, at least in part, on a label of the object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image … the present disclosure may be trained using labeled training data (e.g., using a supervised learning process), p0043-0045];
use the identified one or more features of the object to generate one or more images depicting the object [a multiscale resolution feature map calculator configured to calculate a plurality of feature maps at multiscale resolutions from the core instance features … a semantic segmentation system may generate, for each separate instance of an object in the image (e.g., each instance of a car in the scene), a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object, p0018 & p0044]; and
use the identified one or more features of the object to generate one or more segmentation masks indicating which pixels depict the object in the generated one or more images [a segmentation mask prediction network configured to calculate a plurality of segmentation masks for each detection box of the detection boxes at the multiscale resolutions of the feature maps … a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object. For example, if the semantic segmentation system detects three cars and two pedestrians in the image, five separate instance masks are output: one for each of the cars and one for each of the pedestrians, p0018 & p0044].
Claim 18: El-Khamy discloses the computer system of claim 17, wherein the one or more neural networks include at least one neural network to generate the one or more segmentation masks based, at least in part, on intermediate feature information provided by the one or more neural networks [e.g., semantic segmentation of an image of a street scene may label all of the pixels associated with each car in the scene with the label of “car,” all of the pixels associated with a person on a bicycle with the label “bicycle,” and may label all of the pixels associated with people walking in the scene with the label of “pedestrian.” Furthermore, a semantic segmentation system may generate, for each separate instance of an object in the image (e.g., each instance of a car in the scene), a separate instance mask identifying the pixels of the image that correspond to the separate instance of the object, p0044].
Claim 19: El-Khamy discloses the computer system of claim 17, wherein the one or more neural networks include at least one neural network to synthesize the one or more images of the object [an output module configured to output the instance masks as the detected instances of the objects in the input image, p0018 & p0044].
Claim 20: El-Khamy discloses the computer system of claim 17, wherein at least one of the one or more neural networks is to generate intermediate feature information [the instance semantic segmentation system 10 provides segmentation mask prediction by aggregating all intermediate feature maps of different scales, p0056-0057].
Claim 21: El-Khamy discloses the computer system of claim 17, wherein the one or more images of the object is a subset of a set of images, the subset to be selected using statistical methods [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image, and produces each object detection in the form of a mask of the specific pixels in the image that belong to each instance of the object, a classification of the object category, and a confidence score of the detection … later stage feature maps may be better suited to detect certain kinds of objects, such as very small objects or objects which need a larger global view to understand their semantics … methods for detecting instances of objects in images typically over-detect, and therefore, according to some embodiments of the present disclosure, only those detected instances that satisfy (e.g., exceed) a threshold C (e.g., in terms of confidence score), p0043-0044, p0057 & p0079 – The Examiner notes there is no disclosure as to what statistical method is to be applied. Therefore, in the broadest reasonable interpretation, the citation above is found to apply].
Claim 22: El-Khamy discloses the computer system of claim 17, wherein the one or more images of the object is a subset of a set of images, the subset to be selected based, at least in part, on one or more labels indicating the one or more features of an object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image, and produces each object detection in the form of a mask of the specific pixels in the image that belong to each instance of the object, a classification of the object category, and a confidence score of the detection … later stage feature maps may be better suited to detect certain kinds of objects, such as very small objects or objects which need a larger global view to understand their semantics … methods for detecting instances of objects in images typically over-detect, and therefore, according to some embodiments of the present disclosure, only those detected instances that satisfy (e.g., exceed) a threshold C (e.g., in terms of confidence score), p0043-0046, p0057 & p0079].
Claim 23: El-Khamy discloses the computer system of claim 17, wherein: the one or more images of the object are to be generated by a first neural network of the one or more neural networks [core instance feature extraction network may include a fully convolutional instance semantic segmentation network, p0019]; and the one or more neural networks include at least one neural network with a second neural network that is different from the first neural network [e.g., a fully convolutional instance semantic segmentation network and a segmentation mask prediction network, p0019-0021].
Claim 25: the non-transitory medium having thereon a set of instructions herein have been executed or performed by the one or more processors of claim 1 and is therefore likewise rejected.
Claim 26: El-Khamy discloses the non-transitory machine-readable medium of claim 25, wherein the one or more neural networks are to generate the one or more images of the object based, at least in part on the one or more labels indicating one or more features of an object [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image, and produces each object detection in the form of a mask of the specific pixels in the image that belong to each instance of the object, a classification of the object category, p0043-0046].
Claim 27: El-Khamy discloses the non-transitory machine-readable medium of claim 25, wherein the one or more neural networks are to generate intermediate feature information from the generated one or more images of the object [the instance semantic segmentation system 10 provides segmentation mask prediction by aggregating all intermediate feature maps of different scales, p0056-0057].
Claim 29: El-Khamy discloses the non-transitory machine-readable medium of claim 25, wherein the one or more generated images of the object are selected from a set of images generated by the one or more neural networks, using one or more statistical methods [instance segmentation based on pixel-wise labeling of classes for each instance of an object class in the image, and produces each object detection in the form of a mask of the specific pixels in the image that belong to each instance of the object, a classification of the object category, and a confidence score of the detection … later stage feature maps may be better suited to detect certain kinds of objects, such as very small objects or objects which need a larger global view to understand their semantics … methods for detecting instances of objects in images typically over-detect, and therefore, according to some embodiments of the present disclosure, only those detected instances that satisfy (e.g., exceed) a threshold C (e.g., in terms of confidence score), p0043-0044, p0057 & p0079 – The Examiner notes there is no disclosure as to what statistical method is to be applied. Therefore, in the broadest reasonable interpretation, the citation above is found to apply].
Claim 30: El-Khamy non-transitory discloses the machine-readable medium of claim 25, wherein at least one of the one or more neural networks are trained using a set of labeled images comprising the one or more generated images of the object [present disclosure may be trained using labeled training data (e.g., using a supervised learning process). The training process may be end-to-end by multi-task learning, where the additional information provided by additional deep neural networks directs the instance masks to be of complete, stand-alone objects, which improves performance in case of clutter and occlusion, p0045 & p0056].
Claim 31: El-Khamy non-transitory discloses the machine-readable medium of claim 25, wherein the one or more neural networks are trained based at least in part on the one or more segmentation masks, the label indicating features of the object, and the one or more images of the object [present disclosure may be trained using labeled training data (e.g., using a supervised learning process). The training process may be end-to-end by multi-task learning, where the additional information provided by additional deep neural networks directs the instance masks to be of complete, stand-alone objects, which improves performance in case of clutter and occlusion, p0045 & p0056].
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8, 24 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Khamy et al., (US Pub No. 20190057507) in view of Kataoka et al., (Image generation using generative adversarial networks and attention mechanism, 2016).
Claim 8: El-Khamy discloses the one or more processors of claim 1.
El-Khamy does not appear to disclose wherein the one or more neural networks include at least one generative adversarial network (GAN).
Kataoka discloses in related work [Abstract] wherein the one or more neural networks include at least one generative adversarial network (GAN) [the Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images, Abstract].
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the invention to have included in El-Khamy the support for utilizing at least one or more neural networks include at least one generative adversarial network (GAN) as taught by Kataoka because more realistic images can be generated as discussed by Kataoka in at least the Abstract, Introduction and Conclusion.
Claims 24 and 28: the system and medium herein have been performed or executed by the one or more processors of claim 8 and are therefore likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin, Chinese Pub No. 115115567, discloses obtaining the image to be processed including the target object, dividing the image to be processed, determining the mask image related to the target object and performing feature extraction on the image to be processed.
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 BARBARA D REINIER whose telephone number is (571)270-5082. The examiner can normally be reached M-Tu 10am - 6pm.
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BARBARA D REINIER/ Primary Examiner, Art Unit 2682
Prosecution Insights