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 .
DETAILED ACTION
The instant application having Application No. 18/846,509 filed on September 12, 2024 is presented for examination by the examiner.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted on September 12, 2024 are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statements dated 9/12/2024 and 2/23/2026 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." As a courtesy to applicant the references, U.S. Pat. No. 10,839,515; U.S. Pat. No. 11,257,190 and U.S. Pat. No. 11,361,481 have been considered and have been cited by the examiner on the form PTO-892.
Claim Rejections - 35 USC § 102
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.
Claims 1-5, 7-9, 11-13 and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moult et al. US 2018/0315194 A1 (cited in an IDS, hereafter Moult).
Regarding claim 1, Moult teaches “A method (paragraph [0005]: “an imaging method” see steps below) comprising: generating at least three structural optical coherence tomography (OCT) images of a same location of an object (e.g. paragraph [0005]: “acquiring data of at least three repeated B-scan images of a same location” see also paragraphs [0026]-[0028]: “repeated B-scans”);
generating at least two OCT-Angiography (OCT-A) images (e.g. paragraph [0005]: “generating at least two images based on the acquired data”, paragraph [0025]: “OCT-A acquisition”) based on the structural OCT images (see paragraphs [0025]-[0028] e.g. “OCT-A is based on the idea that if the imaged tissue is stationary between repeated scans, the repeated B-scans will be identical, or very similar. If there is movement within the tissue, the repeated B-scans will differ due to this movement.”), the at least two OCT-A images being based on different interscan times between the corresponding structural OCT images from which the OCT-A images were generated (e.g. paragraph [0005]: “wherein a first of the at least two images is a composite of the acquired data according to the interscan time interval, and a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus one OCTA is generated at a first interscan time interval and the second OCTA is generated at a second interscan time interval that is a multiple of the first interscan time interval);
de-noising the at least two OCT-A images (paragraph [0038]: “the B-scans in these groups were averaged to improve the signal-to-noise ratio and also to minimize statistical fluctuations” thus the OCT-A images constructed from the B-scans have been de-noised by averaging.);
generating a short interscan time (SIT) representative image (e.g. paragraph [0005]: “wherein a first of the at least two images is a composite of the acquired data according to the interscan time interval, and a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus the first interscan time interval is shorter than the second interscan time interval that is a multiple of the first interscan time interval.) and a long interscan time (LIT) representative image based on the at least two OCT-A images (e.g. paragraph [0005]: “a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus the second interscan time interval is longer than the first interscan time interval);
estimating a relative blood flow velocity (paragraph [0044]: “blood flow velocity (the speed and direction (velocity) of blood cells through a blood vessel or other vasculature)” and paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images” thus a “relative” blood flow velocity is determined.) based on the SIT-representative image and the LIT-representative image (paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images”).”
Regarding claim 2, Moult teaches “The method of claim 1, wherein the at least two OCT-A images are cross-sectional B- scans (paragraphs [0005], [0025]-[0027]- three repeated B-scan images of a same location, where B-scans are cross-sectional see right-hand side of Fig. 1).”
Regarding claim 3, Moult teaches “The method of claim 2, further comprising: generating the at least two OCT-A images for a plurality of locations of the object (e.g. paragraph [0065]: “the volumetric OCT-A data of the same interscan time may be averaged and projected through depths of interest (for example, through the depths spanned by the retinal vasculature) to form en face images (here each B-scan location corresponds to a line in the en face image).” En-face images at different depths are different locations of the object), thereby forming a plurality of OCT-A volumes (paragraph [0065]: “volumetric OCT-A data” where different subsets of the depths are a plurality of OCT-A volumes); and
subsequent to de-nosing the at least two OCT-A images, de-noising an en-face image of each of the plurality of OCT-A volumes (e.g. paragraph [0065]: “The en face images may be filtered prior to, during, and/or after further VISTA processing.” and paragraph [0074]+ “The next involves applying a filter 1310 to the input images to smooth random fluctuations that are an inevitable consequence of the randomness inherent in blood flow and the derivative nature of the signal processing”),
wherein the SIT-representative image and the LIT-representative image are based on the denoised en-face images (e.g. paragraph [0038]: “intermediate OCT-A images generated by pairwise comparisons were grouped by interscan time (e.g., as either corresponding to a 1.5 ms interscan time or a 3.0 ms interscan time). Then the B-scans in these groups were averaged to improve the signal-to-noise ratio and also to minimize statistical fluctuations” and paragraph [0065]: “The en face images may be filtered prior to, during, and/or after further VISTA processing.”).”
Regarding claim 4, Moult teaches “The method of claim 1, further comprising generating a blood flow image based on the estimated relative blood flow velocity (e.g. paragraph [0038]: “this increased OCT-A signal is visible at the edges of the margin of geographic atrophy, and indicates a slower blood flow… different interscan OCT-A images can be formed from a single repeated B-scan acquisition to convey information related to blood flow speed and related quantities.” where at least “slower blood flow” indicates a relative blood flow velocity compared to the speed of other blood flows.).”
Regarding claim 5, Moult teaches “The method of claim 3, wherein the blood flow image is a color-mapped image (e.g. paragraph [0005]: “generating a color-mapped image based on the at least two images”) in which pixel color corresponds to the estimated a relative blood flow velocity (e.g. paragraph [0005]: “wherein pixel brightness and pixel color of the color-mapped image each represent one of blood flux and blood flow speed” where blood flow velocities are always “relative blood flow velocity” because there are comparisons between them at the two interscan time intervals.).”
Regarding claim 7, Moulte teaches “The method of claim 1, wherein generating the SIT-representative image comprises statistically combining de- noised OCT-A images having an interscan time less than a predetermined threshold (e.g. paragraphs [0038],[0062]-[0065] and [0073]-[0074]: “the 1.5 ms interscan time” 1.5 ms is less than “a predetermined threshold” in the sense that, for example, it is less than 2 ms, which is “predetermined” in that the choice of 1.5 ms was made prior to the measurement acquisition and processing.); and
wherein generating the LIT-representative image comprises statistically combining de- noised OCT-A images having an interscan time greater than the predetermined threshold (e.g. paragraphs [0038],[0062]-[0065] and [0073]-[0074]: “the 3.0 ms interscan time” 3.0 ms is greater than “a predetermined threshold” in the sense that, for example, it is greater than 2 ms, which is “predetermined” in that the choice of (2 x 1.5 ms) = 3.0 ms was made prior to the measurement acquisition and processing.).”
Regarding claim 8, Moulte teaches “The method of claim 1, wherein the estimated relative blood flow velocity at a given location is a ratio of the SIT-representative image at the given location to the LIT-representative image at the given location (paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images is calculated 1308, thereby forming a color decorrelation image, where each pixel of the color decorrelation image corresponds to the ratio of the corresponding pixels of the input OCT-A images. In calculating this ratio, OCT-A images of different interscan times cannot be interchanged without causing a change in the output. With reference to FIG. 10, a ratio corresponds to transformation ƒ2 and may be defined as
[00007]
f
2
=
r
∆
1
r
∆
2
,
with rΔ1 and rΔ2 being the statistical combinations of each set of OCT-A images D described above.”).”
Regarding claim 9, Moulte teaches “The method of claim 8, wherein estimating the relative blood flow velocity is a pixel- wise determination of the ratio of the SIT-representative image to the LIT-representative image (paragraphs [0038],[0062]-[0065], [0073]: “pixel-by-pixel ratio of the two input OCT-A images”).”
Regarding claim 11, Moulte teaches “The method of claim 1, wherein the object is a retina (e.g. paragraph [0029]: “retinal vasculature”).”
Regarding claim 12, Moult teaches “A method (paragraph [0005]: “an imaging method” see steps below) comprising: generating a plurality of optical coherence tomography angiography (OCT-A) volumes (e.g. paragraph [0005]: “generating at least two images based on the acquired data”, paragraph [0025]: “OCT-A acquisition”), each of the plurality of OCT-A volumes being based on different interscan times (e.g. paragraph [0005]: “wherein a first of the at least two images is a composite of the acquired data according to the interscan time interval, and a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus one OCTA is generated at a first interscan time interval and the second OCTA is generated at a second interscan time interval that is a multiple of the first interscan time interval) between structural OCT images (e.g. paragraph [0005]: “acquiring data of at least three repeated B-scan images of a same location” see also paragraphs [0026]-[0028]: “repeated B-scans”) from which the OCT-A volumes were generated (see paragraphs [0025]-[0028] e.g. “OCT-A is based on the idea that if the imaged tissue is stationary between repeated scans, the repeated B-scans will be identical, or very similar. If there is movement within the tissue, the repeated B-scans will differ due to this movement.”);
de-noising the plurality of OCT-A volumes by (see steps below):
de-noising B-scan images from the plurality of OCT-A volumes (paragraph [0038]: “the B-scans in these groups were averaged to improve the signal-to-noise ratio and also to minimize statistical fluctuations”); and
subsequent to de-noising the B-scan images, de-noising en-face images from the plurality of OCT-A volumes (e.g. paragraph [0065]: “The en face images may be filtered prior to, during, and/or after further VISTA processing. For example, steps to remove OCT-A projection artifacts may be applied… As another example, OCT-A volumes corresponding to different interscan times may be formed by collecting OCT-A images from different slow-scan positions and this data may be volumetrically filtered, for example with a 3-D Gaussian, median filter, and/or vesselness filter, and then mapped to a VISTA volume using a volumetric analogue of ƒ.sub.2. Multiple VISTA images may also be averaged to improve signal-to-noise.”);
generating a short interscan time (SIT) representative image (e.g. paragraph [0005]: “wherein a first of the at least two images is a composite of the acquired data according to the interscan time interval, and a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus the first interscan time interval is shorter than the second interscan time interval that is a multiple of the first interscan time interval. See also e.g. paragraph [0038]: “intermediate OCT-A images generated by pairwise comparisons were grouped by interscan time (e.g., as either corresponding to a 1.5 ms interscan time or a 3.0 ms interscan time)”. Let 1.5 ms be the short interscan time.) by statistically combining de-noised en-face images from OCT-A volumes (e.g. paragraph [0005] “a first of the at least two images is a composite of the acquired data according to the interscan time interval” and paragraphs [0038], [0062]-[0065] and [0073]-[0074]) having an interscan time less than a predetermined threshold (e.g. paragraphs [0038],[0062]-[0065] and [0073]-[0074]: “the 1.5 ms interscan time” 1.5 ms is less than “a predetermined threshold” in the sense that, for example, it is less than 2 ms, which is “predetermined” in that the choice of 1.5 ms was made prior to the measurement acquisition and processing.); and
generating a long interscan time (LIT) representative image by statistically combining de- noised en-face images from OCT-A volumes (e.g. paragraph [0005] “a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval” and paragraphs [0038], [0062]-[0065] and [0073]-[0074]) having an interscan time greater than the predetermined threshold (e.g. paragraphs [0038],[0062]-[0065] and [0073]-[0074]: “the 3.0 ms interscan time” 3.0 ms is greater than “a predetermined threshold” in the sense that, for example, it is greater than 2 ms, which is “predetermined” in that the choice of (2 x 1.5 ms) = 3.0 ms was made prior to the measurement acquisition and processing.).”
Regarding claim 13, Moulte teaches “The method of claim 12, further comprising: estimating a relative blood flow velocity (paragraph [0044]: “blood flow velocity (the speed and direction (velocity) of blood cells through a blood vessel or other vasculature)” and paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images” thus a “relative” blood flow velocity is determined.) based on the SIT-representative image and the LIT-representative image (e.g. paragraph [0005]: “a second of the at least two images is a composite of the acquired data according to a multiple of the interscan interval”. Thus the second interscan time interval is longer than the first interscan time interval); and
generating a blood flow image based on the estimated relative blood flow velocity (paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images is calculated 1308, thereby forming a color decorrelation image, where each pixel of the color decorrelation image corresponds to the ratio of the corresponding pixels of the input OCT-A images”).”
Regarding claim 16, Moulte teaches “The method of claim 12, wherein the object is a retina (e.g. paragraph [0029]: “retinal vasculature”).”
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 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Moult et al. US 2018/0315194 A1 (cited in an IDS, hereafter Moult) as applied to claims 1 and 12 above, and further in view of Mao et al. US 2020/0279352 A1 (cited in an IDS, hereafter Mao).
Regarding claims 6 and 14, Moulte teaches the method of claims 1 and 12, however, Moulte fails to teach “wherein the de-noising is performed by at least one trained machine learning system.”
Mao teaches (claim 1) “A method (see steps below) comprising:
generating … structural optical coherence tomography (OCT) images of a same location of an object (e.g. paragraph [0005]: “the input image is an optical coherence tomography (OCT) or OCT-angiography B-scan image;”);
generating … OCT-Angiography (OCT-A) images (paragraphs [0041]-[0042]: “an angiographic B-scan image… 3D OCT-angiography data”) …
de-noising the at least two OCT-A images (e.g. paragraph [0043]: “Any of the volumetric data may be noise reduced according to the above-described methods”)…
estimating a relative blood flow velocity (paragraph [0038]: “OCT angiography (which relies on differences between images at a common location to indicate blood flow).”).”
Mao further teaches (claims 6 and 14) “wherein the de-noising is performed by at least one trained machine learning system (paragraph [0005]: “the first and/or second noise-reduced images are produced, by a machine learning system;”).”
Mao further teaches paragraph [0020]: “the present disclosure is directed to improvements in noise reduction for coherent imaging modalities. It is now recognized that more than one type of noise exists in images from coherent imaging modalities, and the types and levels of the noise can vary between pixels of an image. These types of noise include: 1) random noise from the system; and 2) speckle variation (noise) caused by the coherent imaging modalities or objects being imaged (e.g., a subject's eye or other biological tissue). The speckle noise may arise from interference of light waves having random phases, for example, as light scattered from various points of turbid object being imaged.”
paragraph [0028] “Artificial intelligence systems such as deep learning (and other machine learning) models/systems can be used to determine the intensity probability distributions for each pixel (e.g., each location of the retina) to more accurately estimate the most probable intensity value of each pixel. The deep learning systems of each filter are particularly designed and trained to estimate the intensity probability distribution of each pixel. The design and training of the deep learning systems are based on an understanding of the fundamental physics in OCT imaging (or the other coherent imaging methods) so that the systems can be trained with images demonstrating the correct intensity probability distributions.”
paragraph [0036] “By way of comparison, combining the outputs of each filter can produce a final noise-reduced image comparable to an image produced by averaging 128 images taken at the same location (a traditional technique for suppressing noise). FIG. 6 shows such a comparison, where an original input image (without any noise reduction) 600, an image produced by averaging 128 images from substantially the same location (including the original input image) 602, and an a noise reduced image 604 resulting from the above-described combination of outputs of a smooth filter and a sharp filter. As can be seen in the entire B-scan, and in the enlarged portions, separately applying different types noise reduction and then combining the outputs of the filters produces comparable or better results to the averaging, both clearly having less noise than the input image. However, with the method described herein, only one B-scan image is needed to be filtered, rather than the many needed if reducing noise by averaging or the perpetuated errors introduced if reducing noise based on a system trained by averaging.”
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a machine-learning based de-noising algorithm as taught by Mao in the method of Moult in order to properly take into account the different sources of noise in the image and obtain a de-noised image from individual scan images, rather than needing to obtain and average a large number of original images as taught by Mao (paragraphs [0020]-[0036]).
Claims 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Moult et al. US 2018/0315194 A1 (cited in an IDS, hereafter Moult) as applied to claims 1 and 12 above, and further in view of Mehrmohammadi et al. US 20180214119 A1 (hereafter Mehrmohammadi) and https://web.archive.org/web/20150109174931/https://en.wikipedia.org/wiki/Gamma_correction (hereafter Gamma Correction).
Regarding claim 10, Moulte teaches “The method of claim 8” however, Moulte fails to explicitly teach “wherein the ratio is raised to a power greater than or equal to 1.5.”
Mehrmohammadi teaches an imaging method “generating one or more images of … venous, and arterial blood flow of respective blood vessels” (paragraph [0009]).
Mehrmohammadi further teaches “raised to a power greater than or equal to 1.5 (paragraph [0054]: “For example, in embodiments, PDU intensity signals are used to estimate fetal blood perfusion and display a strength of returning echoes from the fetal brain to the probe device 100 as described herein and based on a total integrated power for positive and negative velocities. The power of the returning echoes is squared and converted to decibels and displayed in a single color intensity scale. In embodiments, the function may be linear. The PDU intensity signals may be analyzed to determine blood movement” emphasis added.).”
Gamma Correction teaches “raised to a power greater than or equal to 1.5 (pages 1-2: “Gamma correction, gamma nonlinearity, gamma encoding, or often simply gamma, is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems. Gamma correction is, in the simplest cases, defined by the following power-law expression:
V
o
u
t
=
A
V
i
n
γ
where A is a constant and the input and output values are non-negative real values… a gamma value γ > 1 is called a decoding gamma and the application of the expansive power-law nonlinearity is called gamma expansion…
Gamma encoding of images is required to compensate for properties of human vision, hence to maximize the use of the bits or bandwidth relative to how humans perceive light and color. Human vision, under common illumination conditions (not pitch black nor blindingly bright), follows an approximate gamma or power function, with greater sensitivity to relative differences between darker tones than between lighter ones. If images are not gamma-encoded, they allocate too many bits or too much bandwidth to highlights that humans cannot differentiate, and too few bits/bandwidth to shadow values that humans are sensitive to and would require more bits/bandwidth to maintain the same visual quality.” See image with γ=2 on page 3).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to square the ratio
f
2
=
r
∆
1
r
∆
2
of the blood flow velocity images of Moult as taught by Mehrmohammadi and Gamma Correction, because Mehrmohammadi teaches squaring the values of a blood flow image is an appropriate choice for viewing them and Gamma Correction teaches that one can choose the power with which to raise the values within an image in order to maximize the use of the bits or bandwidth relative to how humans perceive light and color such that the viewer is able to perceive the desired features to be highlighted.
Regarding claim 15, Moulte teaches “The method of claim 12, further comprising: estimating a relative blood flow velocity as a pixel-wise determination of a ratio of the SIT-representative image at the given location to the LIT-representative image (paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images is calculated 1308, thereby forming a color decorrelation image, where each pixel of the color decorrelation image corresponds to the ratio of the corresponding pixels of the input OCT-A images. In calculating this ratio, OCT-A images of different interscan times cannot be interchanged without causing a change in the output. With reference to FIG. 10, a ratio corresponds to transformation ƒ2 and may be defined as
[00007]
f
2
=
r
∆
1
r
∆
2
,
with rΔ1 and rΔ2 being the statistical combinations of each set of OCT-A images D described above.”) … and
generating a blood flow image based on the estimated relative blood flow velocity (paragraph [0044]: “blood flow velocity (the speed and direction (velocity) of blood cells through a blood vessel or other vasculature)” and paragraph [0073]: “the pixel-by-pixel ratio of the two input OCT-A images is calculated 1308, thereby forming a color decorrelation image, where each pixel of the color decorrelation image corresponds to the ratio of the corresponding pixels of the input OCT-A images” thus a “relative” blood flow velocity is determined and output as an image.).”
However, Moulte fails to teach “raised to a power greater than or equal to 1.5.”
Mehrmohammadi teaches an imaging method “generating one or more images of … venous, and arterial blood flow of respective blood vessels” (paragraph [0009]).
Mehrmohammadi further teaches “raised to a power greater than or equal to 1.5 (paragraph [0054]: “For example, in embodiments, PDU intensity signals are used to estimate fetal blood perfusion and display a strength of returning echoes from the fetal brain to the probe device 100 as described herein and based on a total integrated power for positive and negative velocities. The power of the returning echoes is squared and converted to decibels and displayed in a single color intensity scale. In embodiments, the function may be linear. The PDU intensity signals may be analyzed to determine blood movement” emphasis added.).”
Gamma Correction teaches “raised to a power greater than or equal to 1.5 (pages 1-2: “Gamma correction, gamma nonlinearity, gamma encoding, or often simply gamma, is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems. Gamma correction is, in the simplest cases, defined by the following power-law expression:
V
o
u
t
=
A
V
i
n
γ
where A is a constant and the input and output values are non-negative real values… a gamma value γ > 1 is called a decoding gamma and the application of the expansive power-law nonlinearity is called gamma expansion…
Gamma encoding of images is required to compensate for properties of human vision, hence to maximize the use of the bits or bandwidth relative to how humans perceive light and color. Human vision, under common illumination conditions (not pitch black nor blindingly bright), follows an approximate gamma or power function, with greater sensitivity to relative differences between darker tones than between lighter ones. If images are not gamma-encoded, they allocate too many bits or too much bandwidth to highlights that humans cannot differentiate, and too few bits/bandwidth to shadow values that humans are sensitive to and would require more bits/bandwidth to maintain the same visual quality.” See image with γ=2 on page 3).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to square the ratio
f
2
=
r
∆
1
r
∆
2
of the blood flow velocity images of Moult as taught by Mehrmohammadi and Gamma Correction, because Mehrmohammadi teaches squaring the values of a blood flow image is an appropriate choice for viewing them and Gamma Correction teaches that one can choose the power with which to raise the values within an image in order to maximize the use of the bits or bandwidth relative to how humans perceive light and color such that the viewer is able to perceive the desired features to be highlighted.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARA E RAKOWSKI whose telephone number is (571)272-4206. The examiner can normally be reached 9AM-4PM ET M-F.
Examiner interviews are available via telephone, in-person, 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, Ricky L Mack can be reached at 571-272-2333. 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.
/CARA E RAKOWSKI/ Primary Examiner, Art Unit 2872
Prosecution Insights