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 .
Specification
The abstract of the disclosure is objected to because it was submitted as part of a cover sheet within the WIPO publication of the application. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1, 2, 4, 6, 10, and 11 objected to because of the following informalities:
The phrase “the plurality of images” in each of the claims should read “the plurality of intravascular images”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 9, 13 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 9, and 13 recite the limitation “the image" in lines 3 and 5 of claim 4 and line 3 of claim 13. There is insufficient antecedent basis for this limitation in the claim. Neither claims 4 or 13 or their independent claims (1 and 10, respectively) explicitly disclose taking an individual image for analysis or entry into the convolutional neural network. Examiner notes that claim 4 recites the limitation “each of a subset of the plurality of images to a convolutional neural network”, however, this is insufficient as the term “each” does not explicitly refer to each image of the plurality of images. Claim 13 recites no such limitation.
Examiner is interpreting this limitation to refer to each image of the subset of the plurality of images. Examiner suggests that Applicant clarifies that “each of a subset” refers to a singular image/an image for the first reference to “the image”
Claim 9 recites the limitation “the catheter tip" in line 4. There is no antecedent basis for this limitation in the claim. Neither claim 9 nor its independent claim, claim 1 disclose a catheter or catheter tip, or any intravascular imaging device in general. Examiner notes that Applicant has support for such a device within the Specification (para. 0019) and thus suggests Applicant recite an imaging device within a catheter tip as a limitation directly within dependent claim 9 or within independent claims 1 or 11 (and, if amending claim 11, changing the dependency of dependent claim 9).
Claim 16 recites the limitation “the polar image" in line 3. There is no antecedent basis for this limitation in the claim. Neither independent claim 10 nor intervening claim 15 disclose a polar image. Examiner notes that Applicant has support for a polar segmented image within the Specification (para. 0019-0020 and 0027-0028) and thus suggests Applicant recite a polar image collection or conversion process within dependent claim 16 or within independent claim 11 or intervening claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. The four eligible categories of invention include: (1) process which is an act, or a series of acts or steps, (2) machine which is an concrete thing, consisting of parts, or of certain devices and combination of devices, (3) manufacture which is an article produced from raw or prepared materials by giving to these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery, and (4) composition of matter which is all compositions of two or more substances and all composite articles, whether they be the results of chemical union, or of mechanical mixture, or whether they be gases, fluids, powders or solids. MPEP 2106(I).
Claims 19-20 are rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention because the claimed invention is directed to computer program per se. Specifically, the limitations of independent claim 19, under the broadest reasonable interpretation, are directed to pure software (in this case, the convolutional neural network and Gaussian process regression model are claimed as products without any structural recitations or physical/tangible form) See MPEP 2106(I). A claim directed toward a non-transitory computer-readable medium having the program encoded thereon establishes a sufficient functional relationship between the program and a computer so as to remove it from the realm of “program per se”. MPEP 2111.05(III). Hence, adding the limitation of “stored on a non-transitory computer-readable medium” would resolve this issue.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 9-10, 14, and 17, as best understood, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yong et al. (“Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography”, Journal of Biomedical optics, Vol. 22, Issue 12, December 2017, hereinafter “Yong”)
Regarding claim 1, Yong describes a method comprising:
acquiring a plurality of intravascular images representing a blood vessel of a patient (pg.126005-2, section 2 subsection 2.l, wherein the dataset acquired comprises a dataset of intravascular optical coherence tomography (IVOCT) images from many patients using two catheter imaging systems).
providing each of a subset of the plurality of images to a convolutional neural network to provide a set of candidate segmentations of one of a lumen boundary and a vessel boundary associated with the blood vessel (pg. 126005-2 section 2.2 for the disclosure of the convolutional neural network to which each of the images, itself a subset of the plurality of images, is input for candidate segmentations of a lumen boundary to be determined, and fig. 1 for a visualization of the full process)
providing the set of candidate segmentations to a regression model to produce a contour of the one of the lumen boundary and the vessel boundary (pg. 126005-2 – 126005-4, sections 2.2 and 2.3, wherein the CNN is a CNN-regression network, wherein the candidate segmentations are produced within the overall CNN architecture, and wherein the radius parameter is inferred in each polar image using regression for contour determination).
Claim 10 is rejected, mutatis mutandis, for reasons similar to claim 1. Yong further discloses an intravascular imaging device that acquires a plurality of intravascular images representing a blood vessel of a patient (pg. 126005-2 section 2 subsection 2.1, wherein the imaging system is one of two imaging systems: the Illumien and Illumien Optis intravascular optical coherence tomography systems).
Regarding claim 6, Yong discloses all limitations of claim 1. Yong further discloses wherein the one of the lumen boundary and the vessel boundary is the lumen boundary (Abstract, “linear regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid”).
Regarding claim 9, Yong discloses all limitations of claim 1. Yong further discloses wherein acquiring the plurality of intravascular images representing the blood vessel of a patient comprises acquiring the plurality of intravascular images as a series of images captured at regular intervals from the catheter tip while the catheter tip is slowly translated through the vessel (pg. 126005-2 section 2 subsection 2.1, wherein the imaging system is one of two imaging systems: the Illumien and Illumien Optis intravascular optical coherence tomography systems, wherein both systems employed catheters for intravascular access and imaging over a variety of pullback scans over a small volume over a 5 second period).
Regarding claim 14, Yong discloses all limitations of claim 1. Yong further discloses wherein the convolutional neural network comprises a series of blocks comprising two convolutional layers, each followed by an activation layer (pgs. 126005-2 – 126005-3 section 2 subsection 2.2, wherein the CNN architecture is disclosed as 4 convolutional layers, each with associated activation layers, 2 fully connected layers, each with associated activation layers, and an output layer; this network satisfies all limitations of the claim, as the ordinarily-skilled artisan could appreciate that the first 2 convolutional layers, the second 2 convolutional layers, and the non-output fully-connected layers could be paired within separate “blocks”, with the underlying architecture remaining the same).
Regarding claim 17, Yong discloses all limitations of claim 1. Yong further discloses wherein the intravascular imaging device is an optical coherence tomography imager (pg. 126005-2 section 2 subsection 2.1, wherein the imaging system is one of two imaging systems: the Illumien and Illumien Optis intravascular optical coherence tomography systems).
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.
Claims 2-3, 7-8, 11-12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yong in view of Cloutier et al. (US PG Pub 20070165916, hereinafter “Cloutier”).
Regarding claims 2 and 11, Yong discloses all limitations of claims 1 and 10, respectively. Yong does not disclose wherein providing each of a subset of the plurality of images comprises applying a gating process to the images to select images associated with a specific point in the cardiac cycle.
However, Cloutier discloses wherein providing each of a subset of the plurality of images comprises applying a gating process to the images to select images associated with a specific point in the cardiac cycle (paras. 0192-0197, directed to retrospective image-based gating of the collected intravascular ultrasound images; images captured at the end of diastole would be most preferential due to their relative lack of artifact and reproducible amounts of volume ).
Specifically, Cloutier discloses a method and system of automatic multi-dimensional segmentation of blood vessel vascular layers from a set of 3D vascular images. The method takes in a set of intravascular ultrasound-obtained images and segments them using a variety of image processing techniques including brightness thresholding and convolution kernels to determine contours of the vessel and lumen. Therefore, both Yong and Cloutier disclose methods and systems of obtaining blood vessel contours based on intravascular image data by processing the image, that processing involving at least a convolution operation. Thus, it would have been obvious to one having ordinary skill in the art to have modified the method and system of Yong to use the retrospective cardiac gating method disclosed by Cloutier as the application of a known technique toa known device ready for improvement, yielding the predictable result of artifact mitigation and a crucial fourth dimension to the analysis of intravascular imagery. More specifically, the use of cardiac gating would enable preferential selection of end-diastolic intravascular images, allowing for “more accurate and reproducible volumic measurements” ,a clear benefit in 4D periodic reconstruction of the interior of the blood vessel (Cloutier paras. 0192-0197).
Regarding claims 3 and 12, Yong and Cloutier disclose all limitations of claims 2 and 11, respectively. Yong does not disclose wherein the specific point in the cardiac cycle is the end of the diastolic stage.
However, Cloutier discloses wherein the specific point in the cardiac cycle is the end of the diastolic stage (paras. 0192-0197, directed to retrospective image-based gating of the collected intravascular ultrasound images; images captured at the end of diastole would be most preferential due to their relative lack of artifact and reproducible amounts of volume ). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have selected end-diastolic stage images in the gating method of Cloutier as implemented within the method and system of Yong according to the rationale of claim 1.
Regarding claim 7, Yong discloses all limitations of claim 1. Yong does not disclose wherein the one of the lumen boundary and the vessel boundary is the vessel boundary. However, Cloutier discloses wherein the one of the lumen boundary and the vessel boundary is the vessel boundary (paras. 0120, 0128-0135, wherein the method of Cloutier can effectively calculate vessel boundaries of the lumen, vessel itself, and intima). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the vessel boundary as the segmentation target and boundary generation target as disclosed by Cloutier as a simple substitution of one image feature target for another. One with ordinary skill in the art would recognize that, outside of changing annotations to determine the vessel boundary as the target (as disclosed by Cloutier) rather than the lumen (as disclosed by Yong), no other changes would have needed to be changed for the method of Yong (as newly modified by Cloutier) to successfully produce the vessel boundary’s contour.
Regarding claim 8, Yong discloses all limitations of claim 1. Yong further discloses wherein the one of the lumen boundary and the vessel boundary is the lumen boundary (Abstract, “linear regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid”).
Yong does not disclose wherein the one of the lumen boundary and the vessel boundary is the vessel boundary.
However, Cloutier discloses wherein the one of the lumen boundary and the vessel boundary is the vessel boundary (paras. 0120, 0128-0135, wherein the method of Cloutier can effectively calculate vessel boundaries of the lumen, vessel itself, and intima).
Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the vessel boundary as the segmentation target and boundary generation target as disclosed by Cloutier and the lumen boundary as disclosed by Yong together as a combination of prior art elements according to known methods (the known methods being using the vessel boundary information of Cloutier as part of the annotations of Yong alongside the annotations of the lumen according to Yong) to yield the predictable result of estimated contours of the lumen and blood vessel using the CNN of Yong.
Regarding claim 18, Yong discloses all limitations of claim 10. Yong does not disclose wherein the intravascular imaging device is an ultrasound transducer. However, Cloutier discloses wherein the intravascular imaging device is an ultrasound transducer (Abstract, “The present invention generally relates to intravascular ultrasound (IVUS) image segmentation methods”; and para. 0065 for explicit disclosure of the ultrasound transducer). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the IVUS transducer of Cloutier in place of the OCT scan of Yong as a simple substitution of one known method of intravascular imaging for another to yield the predictable result of intravascular images using a different scanning apparatus.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yong in view of Chauhan (WIPO PG Pub 2020056454).
Regarding claims 5 and 15, Yong discloses all limitations of claims 1 and 10, respectively. Yong does not disclose wherein the regression model is a Gaussian process regression model.
However, Chauhan discloses wherein the regression model is a Gaussian process regression model (paras. 73-82, specifically disclosing the Gaussian processes regression network, its ability to fit based on the output of a multitude of different learning architectures with a variety of parameters). Chauhan discloses a method and system of retinal image analysis from optical coherence tomography (OCT) scans, specifically relying on finding a foveal center using a CNN-FC network with a subsequent Gaussian processes regression network. Therefore, both Yong and Chauhan disclose a method and system of OCT-based imaging dataset generation and processing, wherein each element of the dataset consists of a circular region with a circular region of interest within each, within which a contour around or of a region or feature of interest must be determined based on a CNN-FC architecture. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the Gaussian processes regression model of Chauhan in place of the linear regression architecture of Yong as the application of a known technique to a known device ready for improvement, yielding the predictable result of a more accurate, robust contour determination using regression.
Allowable Subject Matter
Claims 4, 13, and 16 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROHAN TEJAS MUKUNDHAN whose telephone number is (571)272-2368. The examiner can normally be reached Monday - Friday 9AM - 6PM.
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, Gregory Morse can be reached at 5712723838. 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.
/ROHAN TEJAS MUKUNDHAN/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698