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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-31 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The 112 rejections have been withdrawn.
Applicant argues :
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Examiner’s response:
Applicant’s argument is not persuasive. AAPA does NOT teach away. Teaching away require active criticizing, discrediting or discouraging the use of. Paragraph 39 merely states what is conventionally being done.
Applicant argues :
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Examiner’s response:
Applicant’s argument is not persuasive. The Examiner agrees that AAPA is not a 102 reference and does not employee the specially trained bypass-vessel segmentation model presented by Applicant’s Specification. That detailed model does not appear to be recited in claim 1, or the other independent claims. The human mind reads on a vessel segmentation model as conventionally a person is manually getting a first and second segmentation result using a computer system, as explicitly shown in Paragraph 39. Furthermore the rejection is under 103.
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.
Claim(s) 1-4, 14-16 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Siemionow (PGPub 2020/0320751), hereafter referred to as Siem in view of “Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography”, hereafter referred to as Kiri.
Siem discloses 1. A system, comprising:
at least one storage device including a set of instructions for medical imaging; and
at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations including(Siem, Fig. 7)
obtaining a target image including at least a cardiac region of a subject;(Siem, fig. 1, #101 and Fig.. 2)
determining, based on the target image, a first segmentation result and a second segmentation result, wherein the first segmentation result indicates the heart of the subject segmented from the target image, and the second segmentation result indicates vessels of the subject segmented from the target image; (Siem, Fig. 1 #104 and 107, paragraph 39, “[0039] In step 104 a region of interest (ROI) is extracted by autonomous segmentation of the heart region as outlined by the pericardium. The procedure is performed by three individually trained convolutional neural networks (CNNs), each for processing a particular one of the three sets of 2D slices, namely an axial plane ROI extraction CNN, a sagittal plane ROI extraction CNN and a coronal plane ROI extraction CNN.”;parargraph 73, “[0073] Next, in step 107, the coronary vessel segmentation is performed, preferably individually for each plane, by segmentation CNNs in a similar way as for pericardium, for the two-dimensional slices obtained in the previous step. Therefore, preferably (2N+1)*3 networks are used.”)
Siem discloses using the first and segmentation results to generate a segmented data set, showing vessel shape, location and size (these element can be considered as labels) (Siem, Fig. 1 #104 and 107, paragraph 39, “[0039] In step 104 a region of interest (ROI) is extracted by autonomous segmentation of the heart region as outlined by the pericardium. The procedure is performed by three individually trained convolutional neural networks (CNNs), each for processing a particular one of the three sets of 2D slices, namely an axial plane ROI extraction CNN, a sagittal plane ROI extraction CNN and a coronal plane ROI extraction CNN.”;parargraph 73, “[0073] Next, in step 107, the coronary vessel segmentation is performed, preferably individually for each plane, by segmentation CNNs in a similar way as for pericardium, for the two-dimensional slices obtained in the previous step. Therefore, preferably (2N+1)*3 networks are used.”) (Siem, Fig. 1 #108, paragraph 79, ” [0079] Next, the coronary vessels masks output for the masked slices for the different planes can be combined to a segmented 3D data set representing the shape, location and size of the coronary vessels.”)
But Siem does not expressly disclose
“determining, based on the first segmentation result and the second segmentation result, a target segment result using a vessel segment model, wherein the target segment result includes a plurality of segment labels of a plurality of points on the vessels of the subject, and the plurality of segment labels include a segment label corresponding to bypass vessels; and
determining, based on the target segment result, data relating to one or more bypass vessels of the subject”.
Kiri discloses
“determining, based on the first segmentation result and the second segmentation result, a target segment result using a vessel segment model, wherein the target segment result includes a plurality of segment labels of a plurality of points on the vessels of the subject, and the plurality of segment labels include a segment label corresponding to bypass vessels; and
determining, based on the target segment result, data relating to one or more bypass vessels of the subject” (Kiri pg. 368,
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, thus discloses segment labelling and determining data of the labelled segments. Note: Kiri discloses doing this for all vessels include bypass vessels )
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to label and find properties of the labelled vessels of Siem.
The suggestion/motivation for doing so would have been to provide quantitative measurements to the doctor/reviewer.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Siem with Kiri to obtain the invention as specified in claim 1.
Siem in view of Kiri dislcloses 2. The system of claim 1, wherein the vessels of the subject include coronary arteries. (Siem, Abstract, “A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels ”)
Siem in view of Kiri dislcloses 3. The system of claim 1, wherein the determining, based on the target image, a first segmentation result and a second segmentation result includes:
determining, from the target image, a first region of the cardiac region based on the first segmentation result; and determining, based on the first region of cardiac region, the second segmentation result. (see claim 1)
Siem in view of Kiri dislcloses 4. The system of claim 3, wherein the determining, based on first region of the cardiac region, the second segmentation result includes: determining, from the target image, a second region of the cardiac region other than the first region; and determining the second segmentation result by segmenting vessels from the first second region and the second region.(see claim 1)
Claims 14 and 28 are rejected under similar grounds as claim 1.
Claims 15-16 are rejected under similar grounds as claims 2-3, above respectively.
Claim(s) 1 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Applicant Admitted Prior Art (AAPA) in view of Siemionow (PGPub 2020/0320751), hereafter referred to as Siem
AAPA discloses 1. (Original) A system, comprising: at least one storage device including a set of instructions for medical imaging; and at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations including:
obtaining a target image including at least a cardiac region of a subject; determining, based on the first segmentation result and the second segmentation result, a target segment result using a vessel segment model, wherein the target segment result includes a plurality of segment labels of a plurality of points on the vessels of the subject, and the plurality of segment labels include a segment label corresponding to bypass vessels; and determining, based on the target segment result, data relating to one or more bypass vessels of the subject. (AAPA, “[0039]Conventionally, after a CABG of a patient is completed, a user (e.g., a doctor) needs to manually reconstruct a bypass vessel introduced by the CABG. Specifically, coronary arteries are segmented from an image including a cardiac region of a patient and divided into vessel segments by an existing coronary segmentation and segment system. Since the existing coronary segmentation and segment system is unable to divide the bypass vessel into vessel segment automatically, the user needs to manually determine data relating to the bypass vessel (e.g., a starting point, a path, an anastomotic stoma between the bypass vessel and other vessels) based on the segment result of the coronary arteries for reconstructing an image or a model of the bypass vessel. However, compared with the coronary arteries, the bypass vessel is normally longer and has a more complex trajectory and a poorer imaging visualization. The conventional approach for determining the data relating to the bypass vessel may be inefficient and/or susceptible to human errors or subjectivity. Thus, it may be desirable to develop systems and methods for automatically determining the data relating to the bypass vessel, thereby improving the efficiency and/or accuracy of bypass vessel reconstruction. The terms ?automatic? and ?automated? are used interchangeably referring to methods and systems that analyze information and generates results with little or no direct human intervention.”, Thus AAPA discloses acquiring an image, appears to segment it twice to segment out the bypass vessels (hence labelling it) and determines data relating to the bypass vessel)
, wherein the bypass vessels are formed via a coronary artery bypass surgery performed on the subject (AAPA, Paragraph 39, “Conventionally, after a CABG of a patient is completed, a user (e.g., a doctor) needs to manually reconstruct a bypass vessel introduced by the CABG.”)
AAPA appears to disclose 2 different segmentation algorithms, but it is explicitly clear, therefore that it does not explicitly disclose “determining, based on the target image, a first segmentation result and a second segmentation result, wherein the first segmentation result indicates the heart of the subject segmented from the target image, and the second segmentation result indicates vessels of the subject segmented from the target image; “
Siem discloses ” determining, based on the target image, a first segmentation result and a second segmentation result, wherein the first segmentation result indicates the heart of the subject segmented from the target image, and the second segmentation result indicates vessels of the subject segmented from the target image; “ (Siem, Fig. 1 #104 & #107; see other rejection of claim 1 for additional details)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use the method of Siem to segment the image of AAPA.
The suggestion/motivation for doing so would have been a more accurate segmentation.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine AAPA with Siem to obtain the invention as specified in claim 1.
AAPA in view of Siem discloses 2. The system of claim 1, wherein the vessels of the subject include coronary arteries. (Siem, Abstract, “A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels ”)
AAPA in view of Siem discloses 3. The system of claim 1, wherein the determining, based on the target image, a first segmentation result and a second segmentation result includes:
determining, from the target image, a first region of the cardiac region based on the first segmentation result; and determining, based on the first region of cardiac region, the second segmentation result. (see claim 1)
AAPA in view of Siem discloses 4. The system of claim 3, wherein the determining, based on first region of the cardiac region, the second segmentation result includes: determining, from the target image, a second region of the cardiac region other than the first region; and determining the second segmentation result by segmenting vessels from the first second region and the second region. (Siem, Fig. 1 #104 and 107, paragraph 39, “[0039] In step 104 a region of interest (ROI) is extracted by autonomous segmentation of the heart region as outlined by the pericardium. The procedure is performed by three individually trained convolutional neural networks (CNNs), each for processing a particular one of the three sets of 2D slices, namely an axial plane ROI extraction CNN, a sagittal plane ROI extraction CNN and a coronal plane ROI extraction CNN.”;parargraph 73, “[0073] Next, in step 107, the coronary vessel segmentation is performed, preferably individually for each plane, by segmentation CNNs in a similar way as for pericardium, for the two-dimensional slices obtained in the previous step. Therefore, preferably (2N+1)*3 networks are used.”) (Siem, Fig. 1 #108, paragraph 79, ” [0079] Next, the coronary vessels masks output for the masked slices for the different planes can be combined to a segmented 3D data set representing the shape, location and size of the coronary vessels.”)
AAPA in view of Siem discloses 9. The system of claim 1, wherein the data relating to the one or more bypass vessels includes at least one of:
first data relating to a starting point of each bypass vessel, second data relating to a path of each bypass vessel, or third data relating to an anastomotic stoma between each bypass vessel and coronary arteries. (AAPA, “to manually determine data relating to the bypass vessel (e.g., a starting point, a path, an anastomotic stoma between the bypass vessel and other vessels)”)
Claim 14 is rejected under similar grounds as claim 1 and 4
Claims 28 are rejected under similar grounds as claim 1.
Claims 15 is rejected under similar grounds as claims 2, above.
AAPA in view of Siem discloses 30. (New) The method of claim 14, wherein the data relating to the one or more bypass vessels includes at least one of: first data relating to a starting point of each bypass vessel, second data relating to a path of each bypass vessel, or third data relating to an anastomotic stoma between each bypass vessel and coronary arteries.(AAPA, See paragraph 39)
Allowable Subject Matter
Claims 5-8,10-13,18-19 and 31, 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
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