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 .
Notice to Applications
This communication is in response to the Application filed on June 30, 2022.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on July 8, 2022 and April 18, 2023 are in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statements are being considered and attached by the examiner.
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.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1, (hererinafter “Min”) in view of Aoyama et al., US 20220012878 A1, (hereinafter “Aoyama”).
Regarding claim 2, Min teaches a computer-implemented method of facilitating determination of a cardiovascular risk assessment of a subject based at least in part on image-based analysis of cardiovascular structural dimensions, the computer-implemented method comprising:
analyzing, by the computer system, the medical image to identify one or more cardiovascular structures based at least in part on image segmentation, the one or more cardiovascular structures comprising one or more of aorta, superior vena cava, pulmonary artery, pulmonary veins, right atrium, right ventricle, left atrium, left ventricle, inferior vena cava, left coronary artery, circumflex artery, left anterior descending artery, right coronary artery, pericardium, septum, pulmonary valve, tricuspid valve, aortic valve, or mitral valve ([0168] “Sequentially, in some embodiments, the algorithms that allow for segmentation of atherosclerosis, stenosis and vascular morphology—along with those that allow for segmentation of other cardiovascular structures,”) ([0179] “For example, the one or more additional cardiovascular structures can include the left ventricle, right ventricle, left atrium, right atrium, aortic valve, mitral valve, tricuspid valve, pulmonic valve, aorta, pulmonary artery, inferior and superior vena cava, epicardial fat, and/or pericardium.”);
quantifying, by the computer system, one or more dimensions of the one or more cardiovascular structures, the one or more dimensions comprising one or more of mass, volume, length, area, or diameter ([0182] “In some embodiments, parameters associated with the aorta can include dimensions, volume, diameter, area, enlargement, outpouching, and/or the like. In some embodiments, parameters associated with the pulmonary artery can include dimensions, volume, diameter, area, enlargement, outpouching, and/or the like. In some embodiments, parameters associated with the inferior and superior vena cava can include dimensions, volume, diameter, area, enlargement, outpouching, and/or the like.”);
performing, by the computer system, a comparison of the one or more dimensions of the one or more cardiovascular structures with one or more reference dimensions of one or more cardiovascular structures generated from a plurality of other subjects ([0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects); and
causing, by the computer system, generation of a graphical display of the comparison of the one or more dimensions of the one or more cardiovascular structures with the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects, wherein the graphical display of the comparison is configured to be used to determine a cardiovascular risk assessment of the subject ([0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images. Further, in some embodiments, the system can be configured to generate a visualization of GUI of one or more identified features and/or quantified measurements, such as a quantized color mapping of different features.”) ([0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects),
wherein the computer system comprises a computer processor and an electronic storage medium ([0013] “wherein the computer system comprises a computer processor and an electronic storage medium.”).
Min does not specifically disclose accessing a medical image of a subject, the medical image comprising a representation of a myocardium of the subject.
However, Aoyama teaches accessing a medical image of a subject, the medical image comprising a representation of a myocardium of the subject ([0113] “In another example, the display controlling function 155d may display the FFR and/or the WSS by using polar coordinate display (which may be referred to as a “polar map”) of segments of the myocardia. In this situation, the polar coordinate display of the myocardia segments is presented as an image in which the myocardia are developed and schematically expressed.” wherein a medical image comprising a myocardium is an image displaying the myocardia segments).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the image-based cardiovascular risk analysis method of Min to assessing images representing myocardia of Aoyama because dimensions of the myocardium can provide direct evidence of heart health, thereby further characterizing the risk of cardiovascular disease.
Regarding claim 3, Min in view of Aoyama teaches the computer-implemented method of Claim 2, further comprising:
determining, by the computer system, a structure-specific cardiovascular risk assessment for the one or more cardiovascular structures based at least in part on the comparison of the one or more dimensions of the one or more cardiovascular structures with the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects),
wherein the graphical display comprises the structure-specific cardiovascular risk assessment for the one or more cardiovascular structures (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images. Further, in some embodiments, the system can be configured to generate a visualization of GUI of one or more identified features and/or quantified measurements, such as a quantized color mapping of different features.”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Aoyama teaches the computer-implemented method of Claim 3, wherein the structure-specific cardiovascular risk assessment is determined as one or more of low, medium, or high based at least in part on predetermined thresholds of differences between the one or more dimensions of the one or more cardiovascular structures and the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Aoyama - [0070] “For example, as illustrated in FIG. 2, on the basis of the presence probability of myocardial ischemia extracted by the extracting function 155c, the display controlling function 155d determines the type of information to be displayed on the display 154. In this situation, for example, when the presence probability of myocardial ischemia is higher than the first threshold value set in advance (presence probability: high), the display controlling function 155d determines the type of information to be displayed as the FFR. As another example, when the presence probability of myocardial ischemia is lower than the second threshold value (<the first threshold value) set in advance (presence probability: low), the display controlling function 155d determines the type of information to be displayed as the WSS. As yet another example, when the presence probability of myocardial ischemia falls in the range from the first threshold value to the second threshold value (presence probability: medium), the display controlling function 155d determines the type of information to be displayed as both the FFR and the WSS.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Aoyama teaches the computer-implemented method of Claim 3, wherein the graphical display comprises color-coding of the one or more cardiovascular structures, wherein the color-coding is assigned based at least in part on the structure-specific cardiovascular risk assessment for the one or more cardiovascular structures (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images. Further, in some embodiments, the system can be configured to generate a visualization of GUI of one or more identified features and/or quantified measurements, such as a quantized color mapping of different features.”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Aoyama teaches the computer-implemented method of Claim 3, further comprising generating, by the computer system, one or more proposed treatments for the subject based at least in part on the structure-specific cardiovascular risk assessment for the one or more cardiovascular structures (Min - [0178] “In some embodiments, at block 308, the system can be configured to update the risk of cardiovascular event for the subject based on the comparison to the one or more known datasets. For example, based on the comparison, the system may increase or decrease the previously generated risk assessment. In some embodiments, the system may maintain the previously generated risk assessment even after comparison. In some embodiments, the system can be configured to generate a proposed treatment for the subject based on the generated and/or updated risk assessment”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Aoyama teaches the computer-implemented method of Claim 2, further comprising:
determining, by the computer system, a structure-specific cardiovascular risk assessment for each of the one or more cardiovascular structures based at least in part on the comparison of the one or more dimensions of the one or more cardiovascular structures with the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects); and
generating, by the computer system, a subject-level cardiovascular risk assessment by generating a weighted measure of the structure-specific cardiovascular risk assessment for the one or more cardiovascular structures (Min - [0215] “In some embodiments, the system is configured to generate a risk assessment of coronary disease or cardiovascular event for the subject at block 366 using the weighted measure and/or using only some of these parameters.”),
wherein the graphical display comprises the subject-level cardiovascular risk assessment (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images. Further, in some embodiments, the system can be configured to generate a visualization of GUI of one or more identified features and/or quantified measurements, such as a quantized color mapping of different features.”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Aoyama teaches the computer-implemented method of Claim 7, wherein the subject-level cardiovascular risk assessment is determined as one or more of low, medium, or high based at least in part on predetermined thresholds of differences between the one or more dimensions of the one or more cardiovascular structures and the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Aoyama - [0070] “For example, as illustrated in FIG. 2, on the basis of the presence probability of myocardial ischemia extracted by the extracting function 155c, the display controlling function 155d determines the type of information to be displayed on the display 154. In this situation, for example, when the presence probability of myocardial ischemia is higher than the first threshold value set in advance (presence probability: high), the display controlling function 155d determines the type of information to be displayed as the FFR. As another example, when the presence probability of myocardial ischemia is lower than the second threshold value (<the first threshold value) set in advance (presence probability: low), the display controlling function 155d determines the type of information to be displayed as the WSS. As yet another example, when the presence probability of myocardial ischemia falls in the range from the first threshold value to the second threshold value (presence probability: medium), the display controlling function 155d determines the type of information to be displayed as both the FFR and the WSS.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Aoyama teaches the computer-implemented method of Claim 7, wherein the subject-level cardiovascular risk assessment is determined as a percentile based at least in part on differences between the one or more dimensions of the one or more cardiovascular structures and the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Aoyama - [0065] “The disease degree may be expressed with any value, as long as the value is calculated by the user for the purpose of controlling the display of the FFR and the WSS as described below. For example, the extracting function 155c may directly assign a value serving as a disease degree on the basis of a result of a diagnosing process separately performed for the subject by the user such as a medical doctor or may extract, as a disease degree, a value indicating seriousness of the myocardial ischemia on the basis of a perfusion index obtained from the coronary artery CT image.” wherein a percentile is a value serving as a disease degree) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Aoyama teaches the computer-implemented method of Claim 7, further comprising generating, by the computer system, one or more proposed treatments for the subject based at least in part on the subject-level cardiovascular risk assessment (Min - [0178] “In some embodiments, at block 308, the system can be configured to update the risk of cardiovascular event for the subject based on the comparison to the one or more known datasets. For example, based on the comparison, the system may increase or decrease the previously generated risk assessment. In some embodiments, the system may maintain the previously generated risk assessment even after comparison. In some embodiments, the system can be configured to generate a proposed treatment for the subject based on the generated and/or updated risk assessment”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Aoyama teaches the computer-implemented method of Claim 2, further comprising:
generating, by the computer system, a weighted measure of the comparison of the one or more dimensions of the one or more cardiovascular structures with the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure. For example, in some embodiments, the system can be configured to weight the plaque parameters equally, differently, logarithmically, algebraically, and/or utilizing another mathematical transform. In some embodiments, the system can be configured to utilize only some or all of the quantified plaque parameters.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 12, Min in view of Aoyama teaches the computer-implemented method of Claim 11, further comprising:
generating, by the computer system, a subject-level cardiovascular risk assessment based at least in part on the weighted measure of the comparison of the one or more dimensions of the one or more cardiovascular structures with the one or more reference dimensions of one or more cardiovascular structures generated from the plurality of other subjects (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images.”) (Min - [0177] “In some embodiments, at block 304, the system can be configured to compare the determined one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, to one or more known datasets of coronary values derived from one or more other subjects. The one or more known datasets can comprise one or more vascular morphology parameters, quantified plaque parameters, and/or classified stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or the like, derived from medical images taken from other subjects, including healthy subjects and/or subjects with varying levels of risk.” wherein one or more reference dimensions are the parameters from the one or more known datasets derived from medical images taken from other subjects) (Min - [0231] “In some embodiments, the system can be configured to compare the plaque parameters individually and/or combining one or more of them as a weighted measure. For example, in some embodiments, the system can be configured to weight the plaque parameters equally, differently, logarithmically, algebraically, and/or utilizing another mathematical transform. In some embodiments, the system can be configured to utilize only some or all of the quantified plaque parameters.”),
wherein the graphical display comprises the subject-level cardiovascular risk assessment (Min - [0120] “Based on the identified features and/or quantified measurements, in some embodiments, the system can be configured to generate a risk assessment and/or track the progression of a plaque-based disease or condition, such as for example atherosclerosis, stenosis, and/or ischemia, using raw medical images. Further, in some embodiments, the system can be configured to generate a visualization of GUI of one or more identified features and/or quantified measurements, such as a quantized color mapping of different features.”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 13, Min in view of Aoyama teaches the computer-implemented method of Claim 12, further comprising generating, by the computer system, one or more proposed treatments for the subject based at least in part on the subject-level cardiovascular risk assessment (Min - [0178] “In some embodiments, at block 308, the system can be configured to update the risk of cardiovascular event for the subject based on the comparison to the one or more known datasets. For example, based on the comparison, the system may increase or decrease the previously generated risk assessment. In some embodiments, the system may maintain the previously generated risk assessment even after comparison. In some embodiments, the system can be configured to generate a proposed treatment for the subject based on the generated and/or updated risk assessment”).
The motivation for combining Min and Aoyama is the same motivation as used for claim 2.
Regarding claim 14, the claim recites similar limitations to claim 2 but in the form of a system comprising: a non-transitory computer storage medium configured to at least store computer- executable instructions; and one or more computer hardware processors in communication with the non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to perform the method of claim 2 (Min - [0378] “All of the steps of the process can be performed by embodiments of the system described herein, for example, on embodiments of the systems described in FIG. 13. For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 14 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 15, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 15 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 16, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 17, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 17 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 18, the claim recites similar limitations to claim 10 but in the form of a system. Therefore, claim 18 recites similar limitations to claim 10 and is rejected for similar rationale and reasoning (see the analysis for claim 10 above).
Regarding claim 19, the claim recites similar limitations to claim 11 but in the form of a system. Therefore, claim 19 recites similar limitations to claim 11 and is rejected for similar rationale and reasoning (see the analysis for claim 11 above).
Regarding claim 20, the claim recites similar limitations to claim 12 but in the form of a system. Therefore, claim 20 recites similar limitations to claim 12 and is rejected for similar rationale and reasoning (see the analysis for claim 12 above).
Regarding claim 21, the claim recites similar limitations to claim 2 but in the form of a non-transitory computer readable medium having program instructions for causing a hardware processor to perform a method of claim 2 (Min - [0378] “All of the steps of the process can be performed by embodiments of the system described herein, for example, on embodiments of the systems described in FIG. 13. For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 21 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666