DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s argument regarding the objections to Claims 1 and 3 has been fully considered. The objections to Claims 1 and 3 are withdrawn in view of the amendments.
Applicant’s argument on Pages 11-15 regarding the rejection of Claim 1 under 35 U.S.C. 103 over Mienkina in view of Himsl has been fully considered but is not persuasive under new grounds of rejection as below.
Regarding the rejection of all remaining corresponding claims, applicant’s argument submitted on Page 15 relies on the supposed deficiencies with respect to the rejection of parent Claims 1 and 12. Applicant’s argument is moot for the same reasons detailed above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Canfield et al. (US 20210137416) in view of Bilardo et al. (“ISUOG Practice Guidelines (updated): performance of […]”).
Regarding Claims 1, 12, and 17, Canfield teaches a system for guiding a user in ultrasound assessment of a fetal organ, (Abstract “The present disclosure describes imaging systems configured to generate adaptive scanning protocols based on anatomical features and conditions identified during a prenatal scan of an object.”), said ultrasound assessment being based on an ultrasound image sequence comprising multiple predefined required views of the fetal organ, ([0005] “Systems can also include a user interface configured to display a worklist of required […] imaging views of a fetus” and [0029] “the system 100 enables users to perform effective prenatal assessments by identifying anatomical features within acquired ultrasound image frames 124 and providing instructions to the users for acquiring image data for the next required anatomical feature specified in a worklist”), said system comprising:
a) an input module configured to receive in real time a sequence of two-dimensional ultrasound images comprising multiple predefined required views of the fetal organ, wherein each image comprises at least a portion of the fetal organ ([0027] “The ultrasound data acquisition unit 110 can include an ultrasound probe which includes an ultrasound sensor array 112 configured to transmit ultrasound pulses 114 into a region 116 of a subject, e.g., abdomen, and receive ultrasound echoes 118 responsive to the transmitted pulses. The region 116 may include a developing fetus, as shown, or a variety of other anatomical objects, such as the heart or the lungs. As further shown, the ultrasound data acquisition unit 110 can include a beamformer 120 and a signal processor 122, which can be configured to generate a stream of discrete ultrasound image frames 124 from the ultrasound echoes 118 received at the array 112. The system can also include a data processor 126, e.g., a computational module or circuity, configured to implement a first neural network 128. The first neural network 128 can be configured to receive the image frames 124, either directly from the signal processor 122 or via the data processor 126”); and
b) an image analysis module configured to perform a real-time evaluation, ([0034] “The user interface 134 may be configured to display and update the adaptive scan protocol 138 in real time as an ultrasound scan is being performed.”), by:
i) providing each image as input to an image analysis structure comprising at least one first classifier, said first classifier being configured to identify if the image belongs to any view's category comprised in a predefined list of view's categories and, if so, to identify the view's category to which belongs the image among said predefined list of view's categories ([0027] “The first neural network 128 can be configured to receive the image frames 124, either directly from the signal processor 122 or via the data processor 126, […] and classify an image view, e.g., a current image view, based on this determination.”); wherein each view's category is associated to at least one predefined fetal anatomical landmark, and wherein the predefined list of view's categories comprises ultrasound view categories from medical ultrasound guidelines and the predefined fetal anatomical landmarks comprise physiological fetal landmarks from medical guidelines ([0010] “systems configured to improve the accuracy, efficiency and automation of prenatal ultrasound scans by identifying specific anatomical features, fetal movement and positioning, and in response to such determinations, adaptively guiding a user through a fetal scan in compliance with established medical guidelines” and [0038] “The stored list 150 of required anatomical features can be obtained, in some embodiments, from the American Institute of Ultrasound in Medicine, although established protocols can also be obtained from other entities, e.g., The Society of Obstetricians and Gynecologists of Canada.”);
ii) providing each image as input to a second classifier of the image analysis structure, said second classifier being configured to detect the presence in the image of predefined fetal anatomical landmarks ([0027] “The first neural network 128 can be configured to receive the image frames 124, either directly from the signal processor 122 or via the data processor 126, determine a presence, absence and/or identity of at least one anatomical feature within each frame […] the user interface 134 may receive the outputs directly from the second neural network 130, or after additional processing via the data processor 132. In some examples, the display processor 132 may be configured to generate ultrasound images 136 from the image frames 124, received either directly or indirectly from the data acquisition unit 110, and generate an adaptive scan protocol 138 that can include a worklist updated to reflect the images and/or measurements obtained and the images and/or measurements that have yet to be obtained. In some examples, the scan protocol 138 may convey the presence of an anatomical feature within a current image frame 124, and in some embodiments, whether the image of such feature is sufficient to accurately measure the feature or whether additional images of the feature are needed” and [0035] “A stored list 150 of required anatomical features and specific measurements thereof can also be input into the second neural network 130 […]. The neural networks 128, 130 and modules 146, 148 can be implemented concurrently or successively.”);
iii) whenever the first classifier identifies that the image corresponds to one view's category of the predefined list of view's categories and the second classifier identifies that a predefined number of fetal anatomical landmarks associated to the identified view's category are present in the image, adding said image to a valid images list ([0051] “The 2D or 3D images may be communicated from the scan converter 630, multiplanar reformatter 632, and volume renderer 634 to an image processor 636 for further enhancement, buffering and/or temporary storage for display on an image display 637. Prior to their display, a neural network 638 may be implemented to classify each image based on anatomical features identified therein.”); and
iv) provide the valid images list to be used to perform a diagnostic evaluation of the fetal organ development during a successive medical examination ([0026] “The ultrasound system may include a display or graphics processor, which is operable to arrange the ultrasound image and/or additional graphical information, which may include a worklist of features to be imaged and/or measured, annotations, tissue information, patient information, indicators, and other graphical components, in a display window for display on a user interface of the ultrasound system. In some embodiments, the ultrasound images and tissue information, including information regarding the presence, absence and/or identity of prenatal anatomical features, may be provided to a storage and/or memory device, such as a picture archiving and communication system (PACS) for reporting purposes, developmental progress tracking, or future machine training (e.g., to continue to enhance the performance of the neural network). In some examples, ultrasound images obtained during a scan may be selectively or automatically transmitted, e.g., over a communications network, to a specialist trained to interpret the information embodied in the images, e.g., an obstetrician-gynecologist, an ultrasound specialist, a physician, or other clinician, thereby allowing a user to perform the ultrasound scans necessary for fetal monitoring and/or diagnosis in various locations. The user operating the ultrasound imaging system and the specialist may be located in separate locations during an ultrasound scan, such that transmission of the ultrasound images and/or the information gleaned therefrom may occur over a geographical distance.”).
Furthermore, the cited actions are computer implemented, which necessitate associated computer-readable media, as in [0058] (“components, systems and/or methods are implemented using a programmable device, such as a computer-based system or programmable logic, it should be appreciated that the above-described systems and methods can be implemented using any of various known or later developed programming languages, such as “C”, “C++”, “FORTRAN”, “Pascal”, “VHDL” and the like. Accordingly, various storage media, such as magnetic computer disks, optical disks, electronic memories and the like, can be prepared that can contain information that can direct a device, such as a computer, to implement the above-described systems and/or methods.”).
However, Canfield does not explicitly teach an explicit list of the view's categories comprises ultrasound view categories from medical ultrasound guidelines and the predefined fetal anatomical landmarks comprise physiological fetal landmarks from medical guidelines.
In an analogous 11-14 week fetal ultrasound scan field of endeavor, Bilardo teaches a system for guiding an user in ultrasound assessment of a fetal organ, (General Considerations “It is recommended to use equipment that undergoes regular maintenance and servicing and has at least the following capabilities: real-time, grayscale two-dimensional (2D) ultrasound; color (power) and spectral Doppler; M-mode; transabdominal ultrasound transducers; transvaginal ultrasound transducers; adjustable acoustic power output controls with output display standards; freeze frame and zoom capabilities; electronic calipers; capacity to print/store images.”), wherein the predefined list of view's categories comprises ultrasound view categories from medical ultrasound guidelines and the predefined fetal anatomical landmarks comprise physiological fetal landmarks from medical guidelines (Tables 1-2 and Fig. 2).
It would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the teachings of Canfield with the lists of Bilardo because the combination encourages sound clinical practice, facilitates delivery of optimized antenatal care, which ensures the best possible outcomes for mother and fetus, as taught by Bilardo on Page 127.
Regarding Claims 2 and 13, the modified system of Canfield teaches all limitations of Claim 1, as discussed above. Furthermore, Canfield teaches the input module is configured to further receive the predefined list of view's categories ([0033] “Output generated by the neural network 128 can be input into a second neural network 130, which in some examples, comprises a convolutional neural network (CNN) configured to receive multiple input types. For example, input to the second neural network 130 can include the organ/view classification received from the first neural network 128, along with binary classifications of motion detection, approximated fetal position data, and/or a list of measurements to be obtained in accordance with a stored scan protocol.”).
Moreover, Bilardo teaches wherein each view's category is associated to a view landmarks list comprising at least one predefined view fetal anatomical landmark that should be visible in a view belonging to the view's category, (Table 1, where “Anatomical region” is interpreted as the view category, and the “Minimum requirements for scan” is interpreted as the predefined view fetal anatomical landmark), and wherein the image analysis module is configured to:
a) verify that the first classifier, (taught by Canfield, as discussed above), has identified that the image corresponds to one view's category of the predefined list of view's categories and that a predefined number of the at least one predefined view fetal anatomical landmark, comprised in the view landmarks list associated to the view's category detected by the first classifier, corresponds to the predefined fetal anatomical landmarks detected by the second classifier in the image, so as to evaluate the quality of the image of the identified view category, (Tables 1 and 2), and
b) add said image to the valid images list if both conditions are verified (General Conditions “capacity to print/store images”).
It would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the teachings of Canfield with the lists of Bilardo for the same reasons as Claim 1 above, and further demonstrates a more efficient user experience during the procedure, as less user experience and/or input is required.
Regarding Claim 3, the modified system of Canfield teaches all limitations of Claim 1, as discussed above. Furthermore, Canfield teaches wherein the fetal organ is a fetal heart ([0027] “The region 116 may include a developing fetus, as shown, or a variety of other anatomical objects, such as the heart”).
Regarding Claim 4, the modified system of Canfield teaches all limitations of Claim 1, as discussed above. Furthermore, Canfield teaches wherein in the image analysis module the image analysis structure comprises a first stage employing a convolutional neural network and wherein the first classifier of the image analysis structure comprises a second stage employing a fully connected neural network receiving as input at least a portion of the output of the first stage of the image analysis structure (Fig. 3, [0033] “Output generated by the neural network 128 can be input into a second neural network 130, which in some examples, comprises a convolutional neural network (CNN) configured to receive multiple input types,” and [0040] “the neural network 128 is a convolutional neural network (CNN)”).
Regarding Claim 5, the modified system of Canfield teaches all limitations of Claim 1, as discussed above. Furthermore, Canfield teaches wherein the system further comprises a manual input module configured to receive at least one image provided manually by the user and whenever said image is provided by the user manually, ([0036] “a user may override the low confidence level to proceed with image classification and/or implementation of the second neural network 130” and [0051] “The user interface 624 may also receive […] input requesting that one or more ultrasound images be saved and/or transmitted to a remote receiver.”), the image analysis module is further configured to provide the image as input to the first classifier of the image analysis structure, ([0027] “The first neural network 128 can be configured to receive the image frames 124, either directly from the signal processor 122 or via the data processor 126, […] and classify an image view, e.g., a current image view, based on this determination.”), and the second classifier of the image analysis structure, ([0027] “The first neural network 128 can be configured to receive the image frames 124, either directly from the signal processor 122 or via the data processor 126, determine a presence, absence and/or identity of at least one anatomical feature within each frame […] the user interface 134 may receive the outputs directly from the second neural network 130, or after additional processing via the data processor 132. In some examples, the display processor 132 may be configured to generate ultrasound images 136 from the image frames 124, received either directly or indirectly from the data acquisition unit 110, and generate an adaptive scan protocol 138 that can include a worklist updated to reflect the images and/or measurements obtained and the images and/or measurements that have yet to be obtained. In some examples, the scan protocol 138 may convey the presence of an anatomical feature within a current image frame 124, and in some embodiments, whether the image of such feature is sufficient to accurately measure the feature or whether additional images of the feature are needed” and [0035] “A stored list 150 of required anatomical features and specific measurements thereof can also be input into the second neural network 130 […]. The neural networks 128, 130 and modules 146, 148 can be implemented concurrently or successively.”), and whenever the first classifier identifies that the image corresponds to one view's category of the predefined list of view's categories and the second classifier identifies that a predefined number of fetal anatomical landmarks associated to the identified view's category are present in the image, adding said image to a valid images list ([0026] “The ultrasound system may include a display or graphics processor, which is operable to arrange the ultrasound image and/or additional graphical information, which may include a worklist of features to be imaged and/or measured, annotations, tissue information, patient information, indicators, and other graphical components, in a display window for display on a user interface of the ultrasound system. In some embodiments, the ultrasound images and tissue information, including information regarding the presence, absence and/or identity of prenatal anatomical features, may be provided to a storage and/or memory device, such as a picture archiving and communication system (PACS) for reporting purposes, developmental progress tracking, or future machine training (e.g., to continue to enhance the performance of the neural network). In some examples, ultrasound images obtained during a scan may be selectively or automatically transmitted, e.g., over a communications network, to a specialist trained to interpret the information embodied in the images, e.g., an obstetrician-gynecologist, an ultrasound specialist, a physician, or other clinician, thereby allowing a user to perform the ultrasound scans necessary for fetal monitoring and/or diagnosis in various locations. The user operating the ultrasound imaging system and the specialist may be located in separate locations during an ultrasound scan, such that transmission of the ultrasound images and/or the information gleaned therefrom may occur over a geographical distance.”).
Regarding Claim 6, the modified system of Canfield teaches all limitations of Claim 5, as discussed above. Furthermore, Canfield teaches wherein when the at least one image is provided manually by the user is not validated by the second classifier, the image analysis module is further configured to provide the image as input to an object detector of the image analysis structure comprising a fourth stage configured to receive as input at least a portion of the output of the first stage of the image analysis structure and comprising region-based fully convolutional neural network architecture being configured to perform segmentation of the image, so as to classify and localize fetal anatomical landmarks in the image ([0027] “the output generated by the first neural network 128 may still be input into the second neural network 130, but the two networks may constitute sub-components of a larger, layered network, for example,” [0029] “the system 100 is responsive to the anatomical features detected in a current image view and any current movement and/or position of the fetus, the user can be prompted to obtain images of certain features and, in some examples, required measurements of such features, in a manner that is adaptive to the physical status of the fetus with respect to the position and angular orientation of the data acquisition unit 110,” [0034] “the user input 140 can include instructions for implementing particular operational parameters necessary for imaging […] specific anatomical features,” [0035] “The neural networks 128, 130 and modules 146, 148 can be implemented concurrently or successively,” [0051] “These graphic overlays may contain, e.g., standard identifying information such as patient name, date and time of the image, imaging parameters, and the like, and also various outputs generated by the neural network 638, such as one or more indicators conveying the presence, absence and/or identity of one or more anatomical features embodied in a current image and/or whether various anatomical features have been observed and/or measured and/or which anatomical features have yet to be observed and/or measured in accordance with a stored worklist.”).
Regarding Claim 7, the modified system of Canfield teaches all limitations of Claim 1, as discussed above. Furthermore, Canfield teaches the system further comprising:
a) a diagnostic module, ([0027] “data processor 126”), that when the valid images list comprises all the predefined required views of the fetal organ, ([0027] “a stream of discrete ultrasound image frames 124”), is configured to:
i) provide a stack of one image of the valid images list as input to a diagnostic structure, ([0027] “The first neural network 128 can be configured to receive the image frames 124, […] via the data processor 126”), wherein the diagnostic structure comprises a first stage employing a convolutional neural network receiving as input the stack of images, ([0040] “the neural network 128 is a convolutional neural network (CNN)”), and providing an output and wherein said diagnostic structure comprises a first classifier of the diagnostic structure employing, at a second stage, a fully connected neural network receiving as input at least a portion of the output of the first stage of the diagnostic structure ([0033] “Output generated by the neural network 128 can be input into a second neural network 130, which in some examples, comprises a convolutional neural network (CNN) configured to receive multiple input types. For example, input to the second neural network 130 can include the organ/view classification received from the first neural network 128, along with binary classifications of motion detection, approximated fetal position data, and/or a list of measurements to be obtained in accordance with a stored scan protocol. From these inputs, the second neural network 130 can determine and output a suggested next measurement to be obtained in accordance with the required measurements of a prenatal assessment protocol.”); the first classifier of the diagnostic structure being configured to discriminate between pathological development, ([0041] “the neural network 128 can be configured to determine whether an abnormality is present in an image frame.”), and physiological development of the fetal organ ([0032] “the data processor 126 can be configured to implement a neural network 128, which can be configured to classify images into distinct categories, for example “full view,” “head,” “abdominal,” “chest,” or “extremities.” Sub-categories can include, for example, “stomach,” “bowel,” “umbilical cord,” “kidney,” “bladder,” “legs,” “arms,” “hands,” “femur,” “spine,” “heart,” “lungs,” “stomach,” “bowel,” “umbilical cord,” “kidney,” or “bladder.” Classification results determined by the neural network 128 can be adaptive to a current ultrasound region of interest and/or the completed measurements within the prenatal assessment protocol.”);
ii) whenever the output of the first classifier of the diagnostic structure categorizes the image as comprising a pathological development, providing the image as input to:
1) a second classifier of the diagnostic structure comprising a third stage employing a fully connected neural network, ([0027] “According to such embodiments, the output generated by the first neural network 128 may still be input into the second neural network 130, but the two networks may constitute sub-components of a larger, layered network, for example.”), receiving as input at least a portion of the output of the first stage of the diagnostic structure and being configured to classify the pathological development into at least one pathology category ([0048] “In the event that an abnormality is detected, the user interface 500 may provide an instruction to hold the transducer steady at one location, thereby allowing further analysis. Slight adjustments in the imaging angle may also be recommended to more thoroughly characterize a detected abnormality.”);
2) an object detector of the diagnostic structure comprising a fourth stage configured to receive as input at least a portion of the output of the first stage of the diagnostic structure and comprising a fully convolutional neural network, ([0027] “According to such embodiments, the output generated by the first neural network 128 may still be input into the second neural network 130, but the two networks may constitute sub-components of a larger, layered network, for example.”), being configured to perform segmentation of the image and localization of at least one pathological development region in the fetal organ ([0034] “an adaptive scan protocol 138 that includes a list of required fetal measurements, each of which may be accompanied by a status indicator showing whether or not each measurement has been obtained. The user interface 134 may be configured to display and update the adaptive scan protocol 138 in real time as an ultrasound scan is being performed. In some examples, the user interface 134 may be further configured to display instructions 139 for adjusting the data acquisition unit 110 in the manner necessary to obtain the next recommended measurements. The user input 140 received at the user interface 134 can be in the form of a manual confirmation that a particular measurement has been obtained. In some embodiments, the user input 140 may comprise agreement or disagreement with a next recommended measurement. In this manner, a user may override a recommended measurement. In some examples, the user input 140 can include instructions for implementing particular operational parameters necessary for imaging and/or measuring specific anatomical features, e.g., biparietal diameter, occipito-frontal diameter, head circumference, abdominal circumference, femur length, amniotic fluid index, etc.”);
b) output module, ([0045] “user interface 500”), configured to:
i) output to the user the at least one pathology category obtained from the second classifier of the diagnostic structure, ([0048] “In the event that an abnormality is detected, the user interface 500 may provide an instruction to hold the transducer steady at one location, thereby allowing further analysis. Slight adjustments in the imaging angle may also be recommended to more thoroughly characterize a detected abnormality.”), and the result of the image segmentation of the image and localization of the pathological development region in the fetal organ obtained from the object detector of the diagnostic structure ([0045] “a list of measurements 512 and calculations 514 may also be displayed.”);
ii) output to the user a message to end examination, whenever the output of the first classifier of the diagnostic structure categorizes the image as comprising a physiological development ([0046] “For example, completed measurements can be colored green in the worklist 510, while the next recommended measurements can be colored red, and the current measurement colored blue. In some embodiments, a confidence level associated with a current image view classification and/or the suggested next measurement may also be displayed, for example as a component of the current view description 504.”).
Regarding Claim 8, the modified system of Canfield teaches all limitations of Claim 7, as discussed above. Furthermore, Canfield teaches wherein the fully convolutional neural network of the fourth stage of the object detector is based on a region-based fully convolutional neural network architecture ([0033] “Output generated by the neural network 128 can be input into a second neural network 130, which in some examples, comprises a convolutional neural network (CNN) configured to receive multiple input types.” Where because the CNN of Canfield is utilized to detect objects, it employs region-based fully convolutional neural network architecture.).
Regarding Claim 9, the modified system of Canfield teaches all limitations of Claim 7, as discussed above. Furthermore, Canfield teaches wherein the first of the diagnostic structure, the second classifier of the diagnostic structure and the object detector of the diagnostic structure are configured to receive as input a stack of images comprising at least one image ([0027] “The first neural network 128 can be configured to receive the image frames 124”).
Regarding Claim 10, the modified system of Canfield teaches all limitations of Claim 7, as discussed above. Furthermore, Canfield teaches wherein the first stage convolutional neural networks of the image analysis structure and of the diagnostic structure have at least one common layer, defined during training ([0024] “the neural network(s) may be trained using any of a variety of currently known or later developed machine learning techniques to obtain a neural network (e.g., a machine-trained algorithm or hardware-based system of nodes) that is configured to analyze input data in the form of ultrasound image frames and identify certain features, including the presence and in some embodiments, the size, of one or more prenatal anatomical features” and [0037] “Extracted features may be input into a recurrent neural network, for example, which can be trained to determine a fetal position and/or orientation based on the features identified.”).
Regarding Claim 11, the modified system of Canfield teaches all limitations of Claim 10, as discussed above. Furthermore, Canfield teaches wherein the image analysis structure and of the diagnostic structure results from a simultaneous training, ([0024] “the neural network(s) may be trained using any of a variety of currently known or later developed machine learning techniques to obtain a neural network (e.g., a machine-trained algorithm or hardware-based system of nodes) that is configured to analyze input data in the form of ultrasound image frames and identify certain features, including the presence and in some embodiments, the size, of one or more prenatal anatomical features.”), notably semi-supervised ([0043] “the training may be supervised.).
Regarding Claim 14, the modified method of Canfield teaches all limitations of Claim 12, as discussed above. Furthermore, Canfield teaches when the valid images list comprises all the predefined required views of the fetal organ, further comprises providing a message to inform the user that the valid images list comprises all the predefined required views of the fetal organ ([0051] “These graphic overlays may contain, […] various outputs generated by the neural network 638, such as one or more indicators conveying the presence, absence and/or identity of one or more anatomical features embodied in a current image and/or whether various anatomical features have been observed and/or measured and/or which anatomical features have yet to be observed and/or measured in accordance with a stored worklist.”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA CHRISTINA TALTY whose telephone number is (571)272-8022. The examiner can normally be reached M-Th 8:30-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mike Carey can be reached at (571) 270-7235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA CHRISTINA TALTY/ Examiner, Art Unit 3797
/MICHAEL J CAREY/ Supervisory Patent Examiner, Art Unit 3795
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