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 .
Drawings
The drawings were received on 09/18/2023. These drawings are acceptable.
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, 6, 9, and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kuno et al. US PGPub 2016/0371836 A1 (of record, see IDS dated 09/18/2023, hereinafter, “Kuno”).
Regarding independent claim 1, Kuno discloses a grade evaluation apparatus for blood vessel distribution image (an optical coherence tomography signal processing apparatus is disclosed in at least the title and abstract, and optical coherence tomography produces an angiography image, par. [0007], where an angiography image or angiogram is known in the art to be an image of blood vessels), comprising:
a control device (Fig. 1, controller 70 acquires at least one piece of optical coherence tomography (OCT) data of an OCT signal, motion contrast data, and an enface image from the connected OCT device 10, pars. [0037], [0067], [0071]) configured to:
obtain a blood vessel distribution image (Fig. 1, OCT signal processing apparatus 1 includes display unit 75, par. [0037], and display unit 75 displays an optical coherence tomography image from OCT device 10, par. [0070], and when a subject’s eye E is imaged, CPU 71 displays a screen 80 on display unit 75, see Figs. 5A and 5B, par. [0101], including a motion contrast image as an angiography image, par. [0102], including a distribution of blood vessels, par. [0104]), the blood vessel distribution image representing a distribution of blood vessels in an eyeball obtained by OCT-angiography (Fig. 2, subject eye E is imaged by OCT device 10 as an OCT-angiography image, par. [0102]);
calculate a plurality of evaluation values from the blood vessel distribution image based on a plurality of criteria (controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data on the confirmation screen, par. [0051], where Examiner understands an index to be equivalent to an evaluation value determined from a motion contrast image produced by OCT signal processing apparatus 1, and controller 70 may display an index calculated for each of the plurality of depth region data on the confirmation screen, par. [0052], thereby satisfying the limitation with respect to calculating a plurality of evaluation values, where the index of all layers is obtained by collectively calculating the plurality of depth regions on the calculation screen, par. [0052], and the index for evaluating 3-dimensional motion contrast data may be a ratio of an integrated value of intensity images to an integrated value of the motion contrast images, and the index may be a correlation value between intensity images or may be a correlation value between a plurality of OCT signals when a motion contrast image is generated, pars. [0051-54], and in claim 11, Kuno claims the controller calculates a plurality of indexes for each of the plurality of pieces of depth region data and displays at least one of the plurality of calculated indexes on the confirmation screen, satisfying the limitation); and
calculate a grade value based on the plurality of evaluation values, the grade value indicating a grade of the blood vessel distribution image (Kuno teaches the tester, i.e., the operator of OCT signal processing apparatus 1, can confirm the quality of the 3-dimensional motion contrast data before motion contrast data acquisition is complete, par. [0050], and controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data, par. [0051], and the controller 70 may display an index calculated for each of the plurality of depth region data, par. [0052], and Kuno teaches a motion contrast index indicating the clearness of the motion contrast data, par. [0110], satisfying the limitation).
Regarding dependent claim 6, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value indicating the number of horizontal line artifacts in the blood vessel distribution image (CPU 71 may detect an artifact or the like, for example, a horizontal stripe in a scanning line direction, on the MC image, par. [0136]).
Regarding dependent claim 9, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value indicating sharpness of a blood vessel in the blood vessel distribution image (controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data, where the index indicates clearness of the 3-dimensional motion contrast data, par. [0051], and Kuno teaches a motion contrast index indicating clearness of the motion contrast image so that a tester can determine the quality of the motion contrast data by referencing the index, par. [0110], where Examiner interprets image clarity as discussed by Kuno as being equivalent to sharpness in that the quality of the image produced by apparatus 1 will determine whether blood vessels in the OCT-angiography image are discernible).
Regarding dependent claim 13, Kuno discloses an ophthalmic imaging apparatus for executing optical coherence tomography (OCT), comprising the grade evaluation apparatus according to claim 1 (Fig. 1, OCT signal processing apparatus 1 processes an OCT signal acquired by OCT device 10, par. [0036]).
Regarding independent claim 14, Kuno discloses a non-transitory computer-readable storage medium storing a computer program readable by a computer (Fig. 1, OCT signal processing apparatus 1 includes controller 70, pars. [0036-37], and controller 70 includes central processing unit (CPU) 71 that executes the OCT signal processing apparatus to execute an OCT signal processing program, and controller 70 includes ROM 72 and RAM 73, pars. [0065-67], therefore Kuno discloses a non-transitory computer-readable storage medium in the form of ROM 72 storing a computer program readable by a computer), the computer program, when executed by the computer, causing the computer to perform operations comprising:
obtaining a blood vessel distribution image (Fig. 1, OCT signal processing apparatus 1 includes display unit 75, par. [0037], where display unit 75 displays an optical coherence tomography image from OCT device 10, par. [0070], and when a subject’s eye E is imaged, CPU 71 displays a screen 80 on display unit 75, see Figs. 5A and 5B, par. [0101], including a motion contrast image as an angiography image, par. [0102], including a distribution of blood vessels, par. [0104]), the blood vessel distribution image representing a distribution of blood vessels in an eyeball obtained by OCT-angiography (Fig. 2, subject eye E is imaged by OCT device 10 as an OCT-angiography image, par. [0102]);
calculating a plurality of evaluation values from the blood vessel distribution image based on a plurality of criteria (controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data on the confirmation screen, par. [0051], where Examiner understands an index to be equivalent to an evaluation value determined from a motion contrast image produced by OCT signal processing apparatus 1, and controller 70 may display an index calculated for each of the plurality of depth region data on the confirmation screen, par. [0052], thereby satisfying the limitation with respect to calculating a plurality of evaluation values, where the index of all layers is obtained by collectively calculating the plurality of depth regions on the calculation screen, and the index for evaluating 3-dimensional motion contrast data may be a ratio of an integrated value of intensity images to an integrated value of the motion contrast images, and the index may be a correlation value between intensity images or may be a correlation value between a plurality of OCT signals when a motion contrast image is generated, pars. [0051-54], and in claim 11, Kuno claims the controller calculates a plurality of indexes for each of the plurality of pieces of depth region data and displays at least one of the plurality of calculated indexes on the confirmation screen, satisfying the limitation); and
calculating a grade value based on the plurality of evaluation values, the grade value indicating a grade of the blood vessel distribution image (Kuno teaches the tester, i.e., the operator of OCT signal processing apparatus 1, can confirm the quality of the 3-dimensional motion contrast data before motion contrast data acquisition is complete, par. [0050], and the controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data, par. [0051], and the controller 70 may display an index calculated for each of the plurality of depth region data, par. [0052], and Kuno teaches a motion contrast index indicating the clearness of the motion contrast data, par. [0110], satisfying the limitation).
Regarding independent claim 15, Kuno discloses a grade evaluation method for blood vessel distribution image by a computer including a processor, the grade evaluation method comprising causing the processor to perform:
calculating a plurality of evaluation values based on a plurality of criteria from a blood vessel distribution image (controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data on the confirmation screen, par. [0051], where Examiner understands an index to be equivalent to an evaluation value determined from a motion contrast image produced by OCT signal processing apparatus 1, and controller 70 may display an index calculated for each of the plurality of depth region data on the confirmation screen, par. [0052], thereby satisfying the limitation with respect to calculating a plurality of evaluation values, where the index of all layers is obtained by collectively calculating the plurality of depth regions on the calculation screen, and the index for evaluating 3-dimensional motion contrast data may be a ratio of an integrated value of intensity images to an integrated value of the motion contrast images, and the index may be a correlation value between intensity images or may be a correlation value between a plurality of OCT signals when a motion contrast image is generated, pars. [0051-54], and in claim 11, Kuno claims the controller calculates a plurality of indexes for each of the plurality of pieces of depth region data and displays at least one of the plurality of calculated indexes on the confirmation screen, satisfying the limitation), the blood vessel distribution image representing a distribution of blood vessels in an eyeball obtained by OCT-angiography (Fig. 2, subject eye E is imaged by OCT device 10 as an OCT-angiography image, par. [0102]); and
calculating a grade value based on the plurality of evaluation values, the grade value indicating a grade of the blood vessel distribution image (Kuno teaches the tester, i.e., the operator of OCT signal processing apparatus 1, can confirm the quality of the 3-dimensional motion contrast data before motion contrast data acquisition is complete, par. [0050], and the controller 70 may display an index for evaluating the quality of the 3-dimensional motion contrast data, par. [0051], and the controller 70 may display an index calculated for each of the plurality of depth region data, par. [0052], and Kuno teaches a motion contrast index indicating the clearness of the motion contrast data, par. [0110], satisfying the limitation).
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 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Kuno as applied to claim 1 above, in view of Zhang et al. US PGPub 2019/0110753 A1 (hereinafter, “Zhang”).
Regarding dependent claim 2, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the grade value, the control device is configured to calculate the grade value (see rejection of claim 1 above), but Kuno does not disclose the calculation is by logistic regression using values based on the plurality of evaluation values as explanatory variables.
In a related field of invention, Zhang discloses systems, methods, and devices for carrying out medical diagnosis of ophthalmic diseases and conditions (refer to at least title and abstract thereof). Zhang discloses a computer-implemented method using a machine learning framework for analyzing medical imaging data comprised of ophthalmic images from optical coherence tomography (pars. [0061], [0066-67], and see claim 1 thereof), and Zhang discloses the disclosed algorithm uses a logistic regression method (par. [0082] thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Zhang to the disclosure of Kuno and implemented a logistic regression-based machine learning algorithm in the OCT signal processing program disclosed therein, because a machine learning classifier exhibits performance metrics such as higher accuracy, sensitivity, specificity, positive predictive value, and negative predictive value compared to an average human clinician (Zhang, par. [0073]).
Regarding dependent claim 3, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the grade value, the control device is configured to calculate the grade value (see rejection of claim 1 above), but Kuno does not disclose the calculation is by a decision tree using values based on the plurality of evaluation values as explanatory variables.
In a related field of invention, Zhang discloses systems, methods, and devices for carrying out medical diagnosis of ophthalmic diseases and conditions (refer to at least title and abstract thereof). Zhang discloses a computer-implemented method using machine learning framework for analyzing medical imaging data comprised of ophthalmic images from optical coherence tomography (pars. [0061], [0066-67], and see claim 1 thereof), and Zhang discloses the algorithm uses a decision tree (par. [0082] thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Zhang to the disclosure of Kuno and implemented a decision tree-based machine learning algorithm in the OCT signal processing program disclosed therein, because a machine learning classifier exhibits performance metrics such as higher accuracy, sensitivity, specificity, positive predictive value, and negative predictive value compared to an average human clinician (Zhang, par. [0073]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kuno in view of Zhang as applied to claim 3 above, and further in view of Kubota et al. JP 2017211259 A (where the machine language translation is cited, hereinafter, “Kubota”).
Regarding dependent claim 4, Kuno in view of Zhang discloses the grade evaluation apparatus according to claim 3, and Zhang further teaches wherein the decision tree is a classification tree (Zhang discloses a machine learning platform and decision tree to generate a classifier for diagnosis, pars. [0071-073] and [0082] thereof) and Kuno further discloses classifying the blood vessel distribution image (Kuno discloses the process of confirming quality of 3-dimensional motion contrast data, refer to abstract and pars. [0016], [0020], [0028], [0036], [0039], [0044], [0049-51], [0056], [0060], [0101], [0107], [0110], [0125-126], [0131], [0134], where Examiner assumes the motion contrast data must fall into one of two categories, acceptable or not acceptable, because Kuno teaches the tester, i.e., operator of apparatus 1, can confirm or retry the imaging based on the quality of motion contrast data, par. [0127], thereby implicitly teaching classifying images into two categories or classes).
The prior art combination does not disclose the images are classified into three classes, and therefore does not disclose wherein the grade value takes three types of values respectively corresponding to the three classes (as noted by Examiner, Kuno implicitly teaches the use of at least two classes of images, but does not teach or suggest more than two classes).
In the same field of invention, Kubota discloses an inspection method of an image of an inspection target (refer to par. [0002] of machine translation) where inspection apparatus 100 (refer to at least par. [0016] of machine translation, and see Figs. 2 and 3 of original language document) includes feature extraction unit 158 (refer to at least par. [0025] of machine translation) trained to classify images as “first quality”, “second quality”, “third quality”, “fourth quality”, and “fifth quality” (pars. [0025], [0028], [0035-36] of machine translation). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Kubota to the disclosure of Kuno and modified OCT signal processing apparatus 1 and controller 70 to classify OCT-angiography images into up to 5 categories based on quality of the image, to improve the accuracy of judging whether an object to be inspected, such as the fundus of a patient’s eye, is non-defective or defective (i.e., healthy or diseased) (Kubota, par. [0006] of machine translation).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kuno as applied to claim 1 above, in view of Kingsford, C., Salzberg, S. “What are decision trees?”. Nat Biotechnol 26, 1011–1013 (2008). https://doi.org/10.1038/nbt0908-1011 (hereinafter, “Kingsford”).
Regarding dependent claim 5, Kuno in view of Zhang discloses the grade evaluation apparatus according to claim 3, wherein the decision tree (refer to Zhang par. [0082] teaching a decision tree) is obtained by machine learning using learning data (Zhang teaches the process of performing a machine learning procedure on medical data, refer to at least pars. [0014], [0066], [0082] thereof).
The prior art combination does not explicitly teach or disclose that the decision tree has a node-edge structure. However, as best understood by the Examiner, a decision tree is a type of graph, where a graph is a mathematical structure used to model relationships between objects represented by vertices or nodes of the graph, and therefore, as a type of graph, a decision tree would inherently have nodes, representing questions or choices, connected by edges to outcomes or results. Furthermore, as taught by Kingsford, a decision tree classifies data items (see Fig. 1a thereof) where each question in the decision tree is a node (refer to page 1011 first column thereof) and therefore each node in the decision tree must inherently be connected to another node by an edge for the decision tree to be mathematically useful in a computer program.
With respect to the limitation “the plurality of evaluation values being explanatory variables and the grade value being an objective variable”, the Examiner understands the limitation to be a statement of the relationship between independent variables and dependent variables, where it is well-known that independent variables are parameters that can be controlled or changed, while a dependent variable is the observation or result of the choice of independent, i.e., explanatory, variable. Therefore, the decision tree of Zhang inherently has evaluation values as independent variables in the decision tree, and the results of the decision tree path are the dependent, i.e., objective, values that follow from the decision tree path taken. Therefore, the limitations are disclosed by the prior art as best understood by the Examiner.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kuno as applied to claim 1 above, in view of Imamura et al. US PGPub 2019/0274542 A1 (hereinafter, “Imamura”).
Regarding dependent claim 7, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value (see rejection of claim 1 above), but Kuno does not disclose the calculation of an evaluation value specifically indicating the number of black bands in the blood vessel distribution image.
In the same field of invention, Imamura discloses an image processing apparatus for analyzing tomographic image data of an eye (refer to at least title and abstract thereof). Imamura discloses image processing apparatus 1330 with image processing unit 1333, and unit 1333 includes preprocessing unit 13331 that performs processing of images by detecting and/or removing artifacts (pars. [0314-315] thereof), where a black band is an example of an artifact that preprocessing unit 13331 detects (par. [0345] thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Imamura to the disclosure of Kuno and included a preprocessing unit, such as unit 13331, in the OCT signal processing program disclosed therein, because a black band in an OCTA image is an artifact of decreased decorrelation value, and the decorrelation value depends on eye health and/or type of eye disease (Imamura, pars. [0345-346]).
Claims 8 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kuno as applied to claim 1 above, in view of Abramoff et al. US Patent 9,924,867 B2 (hereinafter, “Abramoff”).
Regarding dependent claim 8, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value (see rejection of claim 1 above) but Kuno does not specifically disclose the evaluation value is indicating a signal-to-noise ratio (SNR) in which a blood vessel in the blood vessel distribution image is treated as a signal and a region other than blood vessel is treated as noise.
In a related field of invention, Abramoff discloses methods and systems for automatically identifying arteries and veins in a region of interest in an image (refer to at least Fig. 8 thereof illustrating a flowchart for a method of automatic determination of arteriovenous ratio, AVR, col. 13, lines 40-47 thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Abramoff to the disclosure of Kuno and included a method for automatically identifying arteries and veins in an OCTA image as provided by OCT signal processing apparatus 1 of Kuno, to provide a parameter, such as AVR, for indicating propensity for a disease based on AVR (Abramoff, col. 13, lines 40-52). As a result, the prior art combination of Kuno in view of Abramoff teaches and renders obvious the limitation wherein a blood vessel in the blood vessel distribution image is treated as a signal and a region other than a blood vessel is treated as noise because the method taught by Abramoff requires the recognition of blood vessels as the equivalent of a signal, and therefore the background of the image would necessarily be noise in the context of this automatic identification process.
Regarding dependent claim 10, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value (see rejection of claim 1 above) but Kuno does not disclose an evaluation value indicating a degree of coupling of blood vessels in the blood vessel distribution image.
In a related field of invention, Abramoff discloses methods and systems for automatically identifying arteries and veins in a region of interest in an image (refer to at least Fig. 8 thereof illustrating a flowchart for a method of automatic determination of arteriovenous ratio, AVR, col. 13, lines 40-47 thereof). Abramoff teaches accurate estimation of AVR by vessel segmentation (col. 5, lines 6-9 thereof), and further teaches the use of a segmentation technique that subdivides the image into areas that are homogeneous based on a certain criterion (col. 7, lines 2-5 thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Abramoff to the disclosure of Kuno and applied a segmentation technique to the OCT-angiography images produced by the apparatus 1 of Kuno, to define a parameter related to homogeneity in the image (Abramoff col. 7, lines 6-8). Additionally or alternatively, Abramoff teaches a vessel segmentation method that assigns each pixel in the image a likelihood that the pixel is within a vessel, producing a so-called vesselness map (col. 5 line 65 to col. 6 line 2 thereof). As a result, the prior art combination teaches and renders obvious the limitation regarding an evaluation value indicating a degree of coupling of blood vessels in the blood vessel distribution image, because as best understood by the Examiner, a parameter related to the homogeneity of an image is equivalent to the degree of coupling of the blood vessels in the image.
Regarding dependent claim 11, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value (see rejection of claim 1 above) but Kuno does not disclose the evaluation value is indicating noise in a horizontal line direction in the blood vessel distribution image.
In a related field of invention, Abramoff discloses methods and systems for automatically identifying arteries and veins in a region of interest in an image (refer to at least Fig. 8 thereof illustrating a flowchart for a method of automatic determination of arteriovenous ratio, AVR, col. 13, lines 40-47 thereof). Abramoff teaches a vessel segmentation method that assigns each pixel in the image a likelihood that the pixel is within a vessel, producing a so-called vesselness map (col. 5 line 65 to col. 6 line 2 thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Abramoff to the disclosure of Kuno and applied a segmentation technique to the OCT-angiography images produced by the apparatus 1 of Kuno, to define a parameter related to homogeneity in the image (Abramoff col. 7, lines 6-8). Additionally or alternatively, Abramoff teaches a vessel segmentation method that assigns each pixel in the image a likelihood that the pixel is within a vessel, producing a so-called vesselness map (col. 5 line 65 to col. 6 line 2 thereof). As a result, the prior art combination teaches and renders obvious the limitation regarding an evaluation value indicating a degree of noise in horizontal lines of images of blood vessels produced by Kuno apparatus 1, because as best understood by the Examiner, a parameter related to the homogeneity of an image is equivalent to a parameter describing horizontal noise in the image.
Regarding dependent claim 12, Kuno discloses the grade evaluation apparatus according to claim 1, wherein in the calculating of the evaluation value, the control device is configured to calculate an evaluation value (see rejection of claim 1 above) but Kuno does not disclose the evaluation value is indicating a level of variation in contrast in the blood vessel distribution image.
In a related field of invention, Abramoff discloses methods and systems for automatically identifying arteries and veins in a region of interest in an image (refer to at least Fig. 8 thereof illustrating a flowchart for a method of automatic determination of arteriovenous ratio, AVR, col. 13, lines 40-47 thereof). Abramoff teaches a vessel segmentation method that assigns each pixel in the image a likelihood that the pixel is within a vessel, producing a so-called vesselness map (col. 5 line 65 to col. 6 line 2 thereof). Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have applied the teachings of Abramoff to the disclosure of Kuno and applied a segmentation technique to the OCT-angiography images produced by the apparatus 1 of Kuno, to define a multi-scale gradient magnitude related that is related to homogeneity in the image, where the gradient magnitude has a maximum at the border of high contrast structures such as vasculature (Abramoff col. 7, lines 6-14), and Abramoff teaches the simultaneous detection of both borders of a vessel by retinal vessel boundary detection based on graph search makes the accurate detection possible even if one boundary is of low contrast (col. 2, lines 45-53 thereof).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mao et al. US PGPub 2020/0279352 discloses an image processing method for coherent imaging modalities, such as optical coherence tomography, to detect abnormalities in OCT angiography images using deep learning systems (refer to at least pars. [0038-40] thereof).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Justin W Hustoft whose telephone number is (571)272-4519. The examiner can normally be reached Monday - Friday 8:30 AM - 5:30 PM Eastern Time.
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, Thomas Pham can be reached at (571)272-3689. 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.
/JUSTIN W. HUSTOFT/ Examiner, Art Unit 2872
/THOMAS K PHAM/ Supervisory Patent Examiner, Art Unit 2872