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 .
Election/Restrictions
Applicant's election of Invention I, Species I (claims 1-5, 7, 11-13, and 17) in the reply filed on 07/08/2026 is acknowledged. Applicant's election is noted with traverse.
Although Applicant points to the shared generic steps recited in claims 1, 7, 11-13, and 17 (obtaining multiple cardiac images; determining local segmentation uncertainties based on the multiple cardiac images; adjusting contour segments based on the uncertainties) and argues that the species groupings are merely compatible refinements of a single core inventive concept, the species within each grouping are set forth as independent and distinct technical features such that the search is diverse for each species ( Species I.A is directed to determining maximum/ minimum stenosis severity profiles by determining a reference set of lumen radius measurements, aligning each set with the reference set, selecting the longest set as the reference, and iteratively aligning/ updating, optionally based on anatomical landmarks. Species I.B is directed to determining maximum/ minimum stenosis severity profiles based on local concavities of multiple curves. Species II.A is directed to adjusting a contour segment by determining whether a local segmentation uncertainty magnitude exceeds a predefined threshold. Species II.B is directed to adjusting a contour segment based on a use case of the lumen segmentation and/or anatomical structure. Species II.C is directed to adjusting a contour segment using a trained machine learning algorithm. Species III.A is directed to obtaining an angiogram and selecting multiple cardiac images among frames based on at least one pre-defined criterion. Species III.B is directed to obtaining multiple cardiac images acquired under different acquisition angles. Species III.C is directed to obtaining multiple cardiac images acquired using multi-phase coronary computed tomography angiography.)
Therefore, while the restricted species may be disclosed as alternative embodiments within the same generic framework, the species within each grouping are directed to different underlying concepts and different technical approaches: a curve-registration and iterative-reference-updating algorithm for profile determination versus a curve-concavity analysis algorithm; a fixed-threshold comparison rule versus a use-case/ anatomy-conditioned rule versus a trained neural-network inference approach for contour adjustment; and a single-angiogram frame-selection-by-criteria approach versus a multi-angle acquisition approach versus a CT-based multi-phase acquisition approach for obtaining the cardiac images. A search directed to one species would not necessarily be expected to identify the most relevant prior art for the other species within the same grouping. For example, a search for iterative curve-to-reference registration and warping techniques for variable-length lumen radius profiles (CPC classes G06T7/33, G06T7/38) would not necessarily locate the most relevant prior art for concavity-based curve analysis (CPC class G06T7/60); likewise, a search for fixed-threshold uncertainty comparison rules (CPC class G06T7/0012) would not necessarily locate the most relevant prior art for trained machine-learning-based contour adjustment using encoder-decoder architectures (CPC classes G06N3/045, G06T7/11); and a search for automated frame-selection criteria applied to a single angiography exam (CPC class G06T7/20) would not necessarily locate the most relevant prior art for multi-phase coronary CT angiography acquisition protocols (CPC class G16H30/40, A61B6/032).
Accordingly, the search for the generic claims would not reasonably encompass the specific subject matter of each dependent species, and examination of all species together would impose a serious search and examination burden. For at least these reasons, and upon reconsideration of Applicant’s traversal, the restriction requirement is still deemed proper and is therefore made FINAL.
Accordingly, examination will proceed on the elected species only. The application has pending claims 1-18 (non-elected claims 6, 8-10, 14-16, and 18 are withdrawn from further consideration).
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show Figure 11 includes reference character “1600” which is not mentioned in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because the final sentence contains the grammatically incorrect phrase “are adjusted edited or manipulated”. The phrase is missing commas in a list of actions, it should be corrected to read “are adjusted, edited, or manipulated”. 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).
The disclosure is objected to because of the following informalities:
[0003]: “extracting determining or calculating” is missing commas in a list of actions, it should be corrected to “extracting, determining, or calculating”
[0007]: contains substantially duplicate consecutive statements “Further, either fully manual annotation of the contour or semi-automated determination of the contour operates on a single cardiac image, e.g., a frame of an X-ray angiogram”. One of the duplicate statements should be deleted.
[00048]: “according to an angiography angel or a fluoroscopy angle” should be corrected to “according to an angiography angle or a fluoroscopy angle”
[00093] and [000108]: “and etc.” should be corrected to “etc.”
[000130]: the reference to location points 1101–1124 “of FIG. 5” should be corrected to “of FIG. 6.”
[000155]: “machine learning algorithm 500” should be corrected to “machine learning algorithm 5000”.
[000158]: contains an incomplete conditional sentence and incorrectly recites a contour “depicting in”. The paragraph should be revised to provide a complete sentence and to refer to the contour as being “depicted in”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 1–5, 7, 11–13, and 17 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.
Independent claims 1 and 17 recite that “each of the multiple cardiac images depicting a portion of coronary arteries” and subsequently refer to “the portion of the coronary arteries”. It is unclear whether each cardiac image must depict the same common portion of the coronary arteries or whether the respective cardiac images may depict different portions of the coronary arteries. This ambiguity affects the scope of the subsequently recited lumen-radius measurements associated with multiple locations of “the portion”, the maximum and minimum stenosis-severity profiles associated with “the portion”, and the local segmentation uncertainties associated with a contour of a lumen segmentation of “the portion”. Although the specification appears to describe the multiple cardiac images as depicting the same portion of the coronary arteries, that requirement is not clearly recited in claims 1 and 17. Applicant may clarify the claims by reciting, for example, that each of the multiple cardiac images depicts the same portion of the coronary arteries. Claims 2–5, 7, and 11–13 depend from claim 1 and inherit the indefiniteness of claim 1.
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.
Claim 1, 7, 11, 13, and 17 are rejected under 35 U.S.C. §103 as being unpatentable over Compas (Compas et al, US 2015/0282777 A1, 2015) in view of Tung (Tung et al, US 2021/0004965 A1, 2021), further in view of Grbic (Grbic et al, US 2016/0267673 A1, 2016).
Regarding claim 1, Compas teaches a computer-implemented method comprising:
obtaining multiple cardiac images, each of the multiple cardiac images depicting a portion of coronary arteries within an anatomical region of interest;
( [0021], [0024]: Compas teaches receiving a sequence of X-ray angiogram images recorded over time, wherein the angiogram images depict coronary arteries. Compas extracts a coronary artery tree from each image and extracts one or more corresponding tubular coronary-artery sections from each image of the sequence; thereby obtaining multiple cardiac images, each depicting a portion of coronary arteries within a coronary region being analyzed. )
determining, based on the multiple cardiac images, respective magnitudes of one or more local segmentation uncertainties, wherein each of the one or more local segmentation uncertainties is associated with a respective segment of a contour of a given lumen segmentation of the portion of the coronary arteries; and
( [0021], [0024], [0030–0032]: Compas teaches extracting and segmenting a coronary artery tree from each image of a sequence of angiogram images and determining arterial-width measurements at corresponding points along respective coronary-artery segments by measuring distances to the segmented vessel boundaries. Compas arranges the arterial-width measurements according to vessel location and image-frame time and analyzes the measurements across the image sequence. Compas determines a width and a magnitude of a local peak associated with a minimum vessel width, wherein the width of the peak indicates the amount by which the location of the minimum varies across the images and the magnitude indicates the persistence of the minimum across the images. Compas further teaches that excessive variance indicates that the apparent stenosis may be caused by an image artifact. Compas’s localized measures of variation and persistence in lumen-width measurements obtained from segmented coronary boundaries are in the same technical context as Applicant’s disclosed local segmentation uncertainties. )
adjusting the respective segments of the contour based on a mean artery tree generated from coronary artery trees extracted from the multiple cardiac images;
( [0021–0023], [Fig. 1, Steps 104–108]: Compas teaches extracting a coronary artery tree from each angiogram image, aligning the extracted artery trees to generate a mean artery tree, and projecting the mean artery tree back onto each angiogram image to recover a complete coronary artery tree. Thus, Compas teaches adjusting or completing respective coronary-artery contour segments based on a mean artery tree generated from coronary artery trees extracted from multiple cardiac images. )
wherein said determining of the respective magnitudes of the one or more local segmentation uncertainties comprises:
determining, based on each of the multiple cardiac images, a respective set of lumen radius measurements comprising multiple lumen radius measurements respectively associated with multiple locations of the portion of the coronary arteries;
( [0024], [0029–0030]: Compas teaches estimating arterial widths for each coronary-artery segment in each angiogram frame by determining distances to the vessel boundary at multiple points along the segment. Compas arranges the measurements according to vessel location and image-frame time, thereby providing a respective set of lumen-width measurements for each cardiac image at corresponding locations along the coronary artery. A lumen radius is directly derivable from the disclosed arterial width. )
determining, based on the respective sets of lumen radius measurements, a spatio-temporal width surface and a minimum in the spatio-temporal width surface associated with the portion of the coronary arteries; and
( [0024–0027], [0030–0032]: Compas teaches determining arterial-width measurements at multiple corresponding points along a coronary-artery segment for each angiogram image, arranging the measurements according to vessel location and image-frame time, and constructing a spatio-temporal surface from the arterial-width measurements. Compas further teaches detecting a persistent minimum in the spatio-temporal surface corresponding to a stenosis in the coronary-artery portion. )
determining the respective magnitudes of the one or more local segmentation uncertainties based on the spatio-temporal variability of lumen width measurements.
( [0024], [0030]–[0032]: Compas teaches arranging lumen-width measurements according to vessel location and image-frame time, generating a spatio-temporal width surface, and evaluating a peak associated with a local minimum. Compas teaches that the width of the peak represents variation in the location of the minimum over time, while the magnitude of the peak represents persistence across the image sequence. )
Compas teaches determining localized segmentation uncertainty from spatio-temporal variation in lumen-width measurements and refining coronary-artery contours, but fails to expressly disclose adjusting a particular contour segment based on its corresponding local uncertainty magnitude, where Tung teaches:
adjusting the respective segments of the contour based on the respective magnitudes of the one or more local segmentation uncertainties;
( [0035–0038]: Tung teaches determining a confidence level for each portion of a clustered segmentation boundary based on the spread of corresponding boundary predictions. When the spread for a boundary portion exceeds a deviation threshold, Tung removes the outlier prediction and recalculates the respective clustered boundary portion using the remaining predictions. Thus, Tung teaches adjusting a respective contour segment based on its local segmentation-uncertainty magnitude. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas’s contour-refinement process to incorporate Tung’s local-confidence-based boundary correction so that contour segments identified as locally unreliable from spatio-temporal measurement variability are selectively recalculated, thereby reducing artifact-induced segmentation errors and improving the accuracy of the coronary-lumen contour. Such a modification would have amounted to the predictable use of a known segmentation-correction technique according to its established function, with a reasonable expectation of success.
Compas [as modified by Tung] teaches determining localized segmentation uncertainty from variations in corresponding lumen-width measurements, but fails to expressly disclose deriving the uncertainty from maximum and minimum stenosis-severity profiles, where Grbic teaches:
determining, based on the respective sets of lumen radius measurements, a maximum stenosis severity profile and a minimum stenosis severity profile associated with the portion of the coronary arteries; and
( [0045–0047], [0051]: Grbic teaches representing anatomical measurements as a range between minimum and maximum values and expressly determining minimum and maximum diameter values based on segmentation-boundary uncertainty. Grbic therefore teaches using corresponding minimum and maximum anatomical measurements to define lower and upper measurement bounds associated with segmentation variation. )
determining the respective magnitudes of the one or more local segmentation uncertainties based on the maximum stenosis severity profile and the minimum stenosis severity profile.
( [0024], [0038], [0040–0042], [Fig. 6]: Grbic teaches estimating uncertainty at respective boundary locations of a segmented anatomical surface, defining minimum and maximum acceptable boundary points for each location, and determining a respective uncertainty value based on the range between the minimum and maximum boundary points. Accordingly, Grbic teaches determining respective local segmentation-uncertainty magnitudes based on corresponding minimum and maximum boundary measurements. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas [as modified by Tung] by applying Grbic’s minimum-and-maximum measurement-range technique to Compas [as modified by Tung]’s corresponding lumen measurements at successive coronary locations to determine maximum and minimum stenosis-severity profiles, and to determine local segmentation uncertainty from the range between those profiles, thereby providing a direct quantitative measure of boundary variation at corresponding coronary locations. Such a modification would have amounted to the predictable use of Grbic’s known minimum-and-maximum uncertainty technique according to its established function, with a reasonable expectation of success.
Regarding claim 7, Compas [as modified by Tung and Grbic] teaches the computer-implemented method of claim 1, said determining of the respective set of lumen radius measurements comprising:
segmenting the portion of the coronary arteries from the respective cardiac image of the multiple cardiac images; and,
( Compas, [0021–0024]: Compas teaches extracting and segmenting a coronary artery tree from each image of a sequence of angiogram images, aligning the extracted artery trees, and projecting a mean artery tree onto each image to recover a complete coronary artery tree in each respective image. )
determining the respective set of the lumen radius measurements based on the respective segmented portion of the coronary arteries.
( Compas, [0024], [0029–0030]: Compas teaches determining arterial-width measurements at multiple points along each segmented coronary-artery section by measuring the distance between vessel-boundary points along a direction normal to the vessel centerline. This yields, for each segmented coronary portion in each cardiac image, determining a respective set of lumen-radius measurements based on each respective segmented coronary-artery portion, because lumen radius is directly derivable from the disclosed arterial width. )
Regarding claim 11, Compas [as modified by Tung and Grbic] teaches the computer-implemented method of claim 1, further comprising:
visualizing, in a cardiac image comprising the given lumen segmentation of the portion of the coronary arteries,
( Tung, [0031], [0041–0050], [Figs. 2–3]: Tung teaches displaying a coronary-vessel segmentation including a boundary between the vessel wall and lumen in a medical image.)
the respective magnitudes of the one or more local segmentation uncertainties.
( Grbic, [0044–0045], [0054], [Fig. 7]: Grbic teaches visually displaying respective local segmentation uncertainties associated with portions of a segmented anatomical boundary, including using different colors or shading to represent respective uncertainty magnitudes.)
Regarding claim 13, Compas [as modified by Tung and Grbic] teaches the computer-implemented method of claim 1, further comprising:
registering the multiple cardiac images.
( Compas, [0022–0023]: Compas teaches aligning [registering] the sequence of angiogram images by calculating a displacement field for each image relative to a selected reference image, including using optical-flow tracking to spatially align corresponding coronary-artery structures across the multiple images. )
Regarding claim 17. The rationale provided for claim 1 incorporated herein. In addition, Compas teaches a computer system comprising one or more processors, system memory, mass storage, and software instructions executable by the processors to perform the disclosed coronary-image-processing operations ( Compas, [0033–0035], [0038–0045] ). Accordingly, the computer-implemented method of claim 1 corresponds to the computer of claim 17 and performs the steps disclosed herein. Therefore, claim 17 is rejected.
Claim 2 is rejected under 35 U.S.C. §103 as being unpatentable over Compas [as modified by Tung and Grbic] in view of Herla (Herla et al, A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting. The Cryosphere, 16(8), 3149–3162, 2022).
Regarding claim 2, Compas [as modified by Tung and Grbic] teaches the computer-implemented method of claim 1, said determining of the maximum stenosis severity profile and the minimum stenosis severity profile comprising:
Compas [as modified by Tung and Grbic] teaches deriving corresponding lumen measurements from multiple coronary images and determining maximum and minimum measurement profiles. Compas further teaches selecting a reference image and aligning the sequence of angiogram images and extracted coronary-artery trees relative to the reference image [0022–0024]. Compas, however, fails to expressly disclose selecting a lumen-radius measurement set as a reference set and aligning each respective lumen-radius measurement set with that reference set, where Herla teaches:
determining a reference set of the lumen radius measurements;
( Herla, [Sec. 2, Fig. 1, pp. 3150–3151]: Herla teaches selecting an initial profile from a plurality of profiles to serve as a reference profile. )
aligning each of the respective sets of the lumen radius measurements with the reference set of the lumen radius measurements; and
( Herla, [Sec. 2, Fig. 1, pp. 3150–3151]: Herla teaches using dynamic time warping to align each individual profile in the dataset with the reference profile, thereby matching corresponding profile elements. )
determining the maximum stenosis severity profile and the minimum stenosis severity profile based on the respective aligned sets of the lumen radius measurements.
( Herla, [Sec. 2, Fig. 1, pp. 3150–3151]; Herla teaches aligning the respective individual profiles with a reference profile such that corresponding elements of the profiles are identified, and subsequently using the corresponding elements of the aligned profiles to determine a resulting profile. Accordingly, Herla teaches using the respective aligned sets of profile measurements as the basis for subsequent profile determination. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas [as modified by Tung and Grbic] to align the respective lumen-radius measurement sets to a selected reference set, as taught by Herla, so that corresponding lumen measurements are consistently associated across the measurement sets before determining the maximum and minimum stenosis-severity profiles. Such a modification would have been the predictable use of a known profile-alignment technique according to its established function, with a reasonable expectation of success.
Claims 3–4 are rejected under 35 U.S.C. §103 as being unpatentable over Compas [as modified by Tung, Grbic, and Herla] in view of Wang (Wang et al, Faster Pan-Genome Construction for Efficient Differentiation of Naturally Occurring and Engineered Plasmids with Plaster. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 19:1-19:12, 2019).
Regarding claim 3, Compas [as modified by Tung, Grbic, and Herla] teaches the computer-implemented method of claim 2, said determining of the reference set of the lumen radius measurements comprising:
Compas [as modified by Tung, Grbic, and Herla] fails to expressly disclose where Wang teaches:
selecting the set of the lumen radius measurements having the longest length as the reference set of the lumen radius measurements.
( Wang, [Sec. 2.1, Fig. 1, pp. 19:3–19:4]: Wang teaches sorting a plurality of sequences according to length and selecting the longest sequence as the initial reference sequence for subsequent alignment. Therefore, Wang teaches using the longest member of a plurality of data sequences as the reference for alignment. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas [as modified by Tung, Grbic, and Herla] to select the longest lumen-radius measurement set as the reference set, as taught by Wang, thereby maximizing reference coverage and reducing unmatched measurement locations during alignment. Such a modification would have been the predictable use of a known longest-first reference-selection technique according to its established function, with a reasonable expectation of success.
Regarding claim 4, Compas [as modified by Tung, Grbic, Herla and Wang] computer-implemented method of claim 3, said aligning of each of the respective sets of the lumen radius measurements with the reference set of the lumen radius measurements comprising:
for each of the respective unaligned sets of the lumen radius measurements, iteratively performing the following:
selecting the longest set of the lumen radius measurements among all the unaligned sets of the lumen radius measurements;
( Wang, [Sec. 2.1, Fig. 1, pp. 19:3–19:4]: Wang teaches sorting respective sequences in descending order of length, selecting the longest sequence as the initial reference, and iteratively selecting the next-longest sequence remaining in the dataset as the next query sequence. Applied to the respective lumen-radius measurement sets, Wang teaches selecting the longest set among the remaining unaligned measurement sets. )
aligning the selected set of the lumen radius measurements with the reference set of the lumen radius measurements; and
( Wang, [Sec. 2.1, Fig. 1, pp. 19:3–19:4]: Wang teaches performing pairwise alignment between each selected query sequence and the current reference sequence. Applied to the respective lumen-radius measurement sets, Wang teaches aligning the selected measurement set with the current reference measurement set. )
updating the reference set of the lumen radius measurements based on all aligned sets of the lumen radius measurements.
( Herla, [Sec. 2, Fig. 1, pp. 3150–3151]: Herla teaches matching the elements of all individual profiles to corresponding elements of a reference profile, averaging the corresponding elements from the aligned profiles, and updating the reference profile using the averaged elements. The matching and reference-updating process is iteratively repeated. Applied to the aligned lumen-radius measurement sets, Herla teaches updating the reference measurement set based on all aligned measurement sets. )
Claim 5 is rejected under 35 U.S.C. §103 as being unpatentable over Compas [as modified by Tung, Grbic, and Herla] in view of Kitslaar (Kitslaar et al, US 2025/0131567 A1, priority date Jan 31st 2022).
Regarding claim 5, Compas [as modified by Tung, Grbic, and Herla] computer-implemented method of claim 2,
Compas [as modified by Tung, Grbic, and Herla] fails to expressly disclose where Kitslaar teaches:
wherein said aligning is based on one or more anatomical landmarks within the anatomical region of interest.
( [0006–0007], [0016], [0051], [0062–0065], [0069]: Kitslaar teaches identifying corresponding points of interest or vessel landmarks within coronary-vessel datasets, including side branches, deviations in lumen size, shape, or area, and calcified matter. Kitslaar teaches matching and aligning the datasets by associating points of interest that identify the same anatomical feature and registering corresponding locations between the landmarks; thereby aligning lumen-measurement data based on one or more anatomical landmarks within the coronary anatomical region of interest. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas [as modified by Tung, Grbic, and Herla] to align the lumen-radius measurement sets using corresponding anatomical landmarks, as taught by Kitslaar, thereby improving correspondence between measurements obtained from the same coronary locations. Such a modification would have been the predictable use of a known landmark-based cardiovascular-data alignment technique according to its established function, with a reasonable expectation of success.
Claim 12 is rejected under 35 U.S.C. §103 as being unpatentable over Compas [as modified by Tung and Grbic] in view of Zhong (Zhong et al, US 2019/0029625 A1, 2019).
Regarding claim 12, Compas [as modified by Tung and Grbic] teaches the computer-implemented method of claim 1, further comprising:
Compas [as modified by Tung and Grbic] fails to expressly disclose where Zhong teaches:
determining the anatomical region of interest based on multiple predefined seed points.
( Zhong, [0082–0083], Fig. 10: Zhong teaches defining a coronary-artery segment of interest by designating two endpoints on the segmented artery and determining a centerline connecting the two fixed endpoints; thereby determining an anatomical region of interest based on multiple predefined seed points. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Compas [as modified by Tung and Grbic] to define the coronary-artery region of interest using Zhong’s two predefined endpoints, thereby limiting segmentation and measurement processing to the selected vessel segment and improving the efficiency and accuracy of coronary analysis. Such a modification would have been the predictable use of a known coronary-segment-selection technique according to its established function, with a reasonable expectation of success.
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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