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 Objection
Figure 1 is objected to as depicting a block diagram without “readily identifiable” descriptors of each block, as required by 37 CFR 1.84(n). Rule 84(n) requires “labeled representations” of graphical symbols, such as blocks; and any that are “not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.” In the case of figure 1, the blocks are not readily identifiable per se and therefore require the insertion of text that identifies the function of that block. That is, each vacant block should be provided with a corresponding label identifying its function or purpose.
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.
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 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.
Claims 1-3, 5, 9-13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cherkezyan et al. (US20230145920, hereinafter “Cherkezyan”) and in view of Tsymbalenko et al (US20200121294, hereinafter “Tsymbalenko”)
Claim 1. Cherkezyan teaches A method for evaluating an exploitability of 4D-tomographic image data, ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).” And [0003] “over time to generate a plurality of slices from which one or more images may be generated.” When 3D tomography is acquired over time it is understood to be the same as the claimed 4D tomographic image data) the method comprising:
receiving 4D-tomographic image data, wherein said 4D-tomographic image data includes a plurality of 3D-tomographic image data of an examination object, ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).”) and wherein the plurality of 3D-tomographic image data corresponds to a plurality of time points; ([0003] “over time to generate a plurality of slices from which one or more images may be generated.” And [0053] “image reconstruction technique such that the projections acquired at time points”)
comparing the at least one scoring value with a threshold value; ([0068] “determining that the inconsistency metric is greater than a second threshold”)
and providing a user notification, when at least one of the at least one scoring value exceeds the threshold value. ([0068] “taking an action based on the inconsistency metric comprises determining that the inconsistency metric is greater than a second threshold, and in response, outputting a notification of patient motion on a display device.” Inconsistency metric is understood to be the same as the claimed scoring value) and wherein the at least one scoring value at least one of includes or corresponds to a metric quantifying an extent to which a vicinity of voxels at a surface contains an image artifact; ([0067] “subject motion may be detected in a manner that is highly sensitive to temporal data inconsistencies that cannot be detected with current temporally-resolved sinogram- or voxel-tracking methods, identifies only the inconsistencies/motion that have led to artifacts”)
Cherkezyan does not explicitly teach applying a segmentation algorithm to the plurality of image data, wherein the segmentation algorithm is configured to segment at least one organ in the plurality of image data to which the segmentation algorithm is applied;
applying a scoring function to the at least one segmented organ in the plurality of image data, wherein the scoring function is configured to determine at least one scoring value for the at least one segmented organ to which the scoring function is applied,
Tsymbalenko teaches applying a segmentation algorithm ([0024] “A tracking boundary may be associated with each identified object in an image frame. The tracking boundaries may have suitable geometries, such as square, rectangular, circular, polyhedron, etc. The geometries of the tracking boundaries may the same for each identified object,” tracking boundary is understood to be the same as the calimed segmentation) to the plurality of 3D-tomographic image data, ([0023] “The object detector 117 may generate an indication of the position of each identified object in each image frame” [0038] “the object detection and motion scores may be performed on three-dimensional volume data.”) wherein the segmentation algorithm is configured to segment at least one organ ([0026] “ tracking boundary and hence the identified object (e.g., organ)”) in the plurality of 3D-tomographic image data to which the segmentation algorithm is applied; ([0019] “object detector 117 may analyze each image frame acquired with the ultrasound imaging system 100 and identify anatomical features within each image frame, such as a heart, liver, lungs, blood vessels, and/or other organs, tissue, and/or structure.” )
applying a scoring function to the at least one segmented organ in the plurality of image data, wherein the scoring function is configured to determine at least one scoring value for the at least one segmented organ to which the scoring function is applied, ([0025] “determine a motion score based on a difference between the position of the tracking boundary in the second image frame and the position of the tracking boundary in the first image frame.” And [0026] “The motion score represents the change in position of the tracking boundary and hence the identified object (e.g., organ)”) and wherein the at least one scoring value at least one of includes or corresponds to a metric quantifying an extent to which at least one segmented organ contains an image artifact; ([0059] “Flash artifacts are the presence of a color signal in color flow imaging of color B-flow imaging that may be caused by tissue motion… imaging derived pixels in the region of the identified object with the high motion score and overriding any color pixels that would otherwise be displayed in the region of the identified object.”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Cherkezyan to have applying a segmentation algorithm to segment an organ and apply a scoring function to determine if it contains an artifact as taught by Tsymbalenko to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been so that (Tsymbalenko et al [0011] “image artifacts may be reduced in a manner that is most appropriate for the individual objects being tracked.”)
Claim 2. Cherkezyan and Tsymbalenko teach The method according to claim 1,
Cherkezyan teaches wherein the 4D-tomographic image data ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).” And [0003] “over time to generate a plurality of slices from which one or more images may be generated.” When 3D tomography is acquired over time it is understood to be the same as the claimed 4D tomographic image data) includes N number of 3D-tomographic image data, ([0003] “The images reconstructed from the slices may be combined to create a 3D volumetric image of the ROI.”) the segmentation algorithm is applied to the N number of 3D-tomographic image data, ([0055] “the soft tissue masks may be applied to each image individually”) and in the applying the scoring function, at least N number of scoring values are determined. ([0068] “calculating an inconsistency metric quantifying temporal inconsistencies between the first image and the second image,” and [0068] “calculating an additional inconsistency metric for each additional subset of projection data of the entire volume of projection data of the imaging subject”)
Claim 3. Cherkezyan and Tsymbalenko teach The method according to claim 1,
Cherkezyan teaches 3D-tomographic image data ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).”)
Cherkezyan does not explicitly teach wherein the segmentation algorithm is configured to segment M number of organs in the plurality of to which the segmentation algorithm is applied, and
in the applying the scoring function, at least M number of scoring values are determined for at least one of each of the plurality of time points or each of the plurality of image data.
Tsymbalenko teaches wherein the segmentation algorithm is configured to segment M number of organs ([0010] “automatically identifying one or more objects present in a medical image and independently tracking motion of those objects across two or more consecutive images…The objects may include separate anatomical features, such as organs,”) in the plurality of image data to which the segmentation algorithm is applied, ([0019] “Each frame may be tagged with an indication of the object(s) identified in that image” ) and
in the applying the scoring function, at least M number of scoring values
are determined for at least one of each of the plurality of time points or each of the plurality of image data. [0026] “The motion score represents the change in position of the tracking boundary and hence the identified object …tracking boundary is tracked across multiple image frames).” And [0017] “frames of data are stored in a manner to facilitate retrieval thereof according to its order or time of acquisition” when scores are given for each frame and frames are captured in the order of time then it is understood that an M number of scoring values will be determined for each plurality of time points )
Claim 5. Cherkezyan and Tsymbalenko teach The method according to claim 1,
Cherkezyan teaches 4D-tomographic image data ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).” And [0003] “over time to generate a plurality of slices from which one or more images may be generated.” When 3D tomography is acquired over time it is understood to be the same as the claimed 4D tomographic image data)
Cherkezyan does not explicitly teach Wherein the image data includes a plurality of segmentable organs, the segmentation algorithm is configured to segment a subsection of the plurality of segmentable organs of the image data, and the subsection of plurality of segmentable organs includes organs with at least one of a highest contrast or that are most error prone to motion artefacts.
Tsymbalenko teaches Wherein the image data includes a plurality of segmentable organs, the segmentation algorithm is configured to segment a subsection of the plurality of segmentable organs of the image data, and the subsection of plurality of segmentable organs includes organs with at least one of a highest contrast or that are most error prone to motion artefacts. ([0027] “A separate motion score may be calculated for each identified object in the second image frame. By separately calculating motion scores for each identified object, objects that have different levels of movement (e.g., organs close to the heart versus organs further away from the heart) may be assigned motion scores that accurately reflect that object's level of movement.” is understood to be the same as the claimed organs that are most error prone to motion artifacts in light of instant specifications [0041])
Claim 9. Cherkezyan and Tsymbalenko teach The method according to claim 1, further comprising:
Cherkezyan teaches generating the user notification when at least one of the at least one scoring value exceeds the threshold value, ([0053] “If instead the IM is greater than the threshold at 414, … the operator is notified at 420. For example, a notification may be output for display on a display device of the CT imaging system indicating patient motion”) wherein
the user notification includes a time point related to at least one of a 3D-tomographic image data with the image artifact, related 3D-tomographic image data or an overlay image, ([0066] “The inconsistency metric disclosed herein identifies and quantifies any temporal inconsistencies in acquired tomographic data (including those due to patient motion, table speed variations, data drift, etc.) as well as specifies the axial images affected by the temporal inconsistencies.”) and
the overlay image includes at least one of a related 3D-tomographic image, ([0066] “specifies the axial images affected by the temporal inconsistencies.”) an indication of the image artifact or a location of the image artifact. ([0066] “notify the operator of the imaging system or other clinician of the presence and axial location of the artifact(s)”)
Claim 10. Cherkezyan and Tsymbalenko teach The method according to claim 1,
Cherkezyan does not explicitly teach wherein at least one of the segmentation algorithm or the scoring function is configured as at least one of a machine learned algorithm or a machine learned function.
Tsymbalenko teaches wherein at least one of the segmentation algorithm or the scoring function is configured as at least one of a machine learned algorithm or a machine learned function. ([0020] “ The object detector may be trained to detect a plurality of predefined objects (e.g., predefined anatomical features) using machine learning (e.g., deep learning), such as neural networking or other training mechanisms that are specific to object detection in a medical imaging environment.”)
Claim 11. Cherkezyan and Tsymbalenko teach The method according to claim 1,
Cherkezyan teaches wherein the providing the user notification includes at least one of providing or showing the user notification at least one of to or on a scanner console of a scanner used to acquire the 4D-tomographic image data. ([0053] “a notification may be output for display on a display device of the CT imaging system”)
Claim 12. Cherkezyan and Tsymbalenko teach to perform the method as claimed in claim 1.
Cherkezyan teaches A non-transitory computer-readable storage medium storing computer program code that, when executed by a computer processor, causes the computer processor ([0045] “Method 400 may be carried out according to instructions stored in non-transitory memory of a computing device, such as computing device 216 of FIG. 2.”)
Claim 13. Cherkezyan and Tsymbalenko teach the method according to claim 1.
Cherkezyan teaches A scanner device ([0023] “the CT system 100 further includes an image processor unit” and [0029] “FIG. 2 illustrates an exemplary imaging system 200 similar to the CT system 100 of FIG. 1…. MDCT Scanner”)comprising:
at least one processor configured to perform the method according to claim 1. ([0045] “Method 400 may be carried out according to instructions stored in non-transitory memory of a computing device, such as computing device 216 of FIG. 2.”)
Claim 20. Cherkezyan teaches A scanner device ([0023] “the CT system 100 further includes an image processor unit” and [0029] “FIG. 2 illustrates an exemplary imaging system 200 similar to the CT system 100 of FIG. 1…. MDCT Scanner”)comprising:
a memory storing computer-executable instructions; and
at least one processor configured to execute the computer-executable instructions to cause the scanner device ([0045] “Method 400 may be carried out according to instructions stored in non-transitory memory of a computing device, such as computing device 216 of FIG. 2.”) wherein the plurality of 3D-tomographic image data is included in 4D-tomographic image data, ([0002] “acquire image data and to construct tomographic images (e.g., three-dimensional (3D) representations of the interior of the human body or of other imaged structures).”) wherein the plurality of 3D-tomographic image data corresponds to a plurality of time points, ([0003] “over time to generate a plurality of slices from which one or more images may be generated.” And [0053] “image reconstruction technique such that the projections acquired at time points”)
and wherein the at least one scoring value at least one of includes or corresponds to a metric quantifying an extent to which a vicinity of voxels at a surface of the at least one segmented organ in the plurality of 3D-tomographic image data contains an image artifact, ([0067] “subject motion may be detected in a manner that is highly sensitive to temporal data inconsistencies that cannot be detected with current temporally-resolved sinogram- or voxel-tracking methods, identifies only the inconsistencies/motion that have led to artifacts”)
compare the at least one scoring value with a threshold value, ([0068] “determining that the inconsistency metric is greater than a second threshold”)and
provide a user notification, when at least one of the at least one scoring value exceeds the threshold value. ([0068] “taking an action based on the inconsistency metric comprises determining that the inconsistency metric is greater than a second threshold, and in response, outputting a notification of patient motion on a display device.” Inconsistency metric is understood to be the same as the claimed scoring value)
Cherkezyan does not explicitly teach apply a segmentation algorithm to a plurality of image data of an examination object, and wherein the segmentation algorithm is configured to segment at least one organ in the plurality of image data to which the segmentation algorithm is applied,
apply a scoring function to the at least one segmented organ, wherein the scoring function is configured to determine at least one scoring value for the at least one segmented organ to which the scoring function is applied,
Tsymbalenko teaches apply a segmentation algorithm ([0024] “A tracking boundary may be associated with each identified object in an image frame. The tracking boundaries may have suitable geometries, such as square, rectangular, circular, polyhedron, etc. The geometries of the tracking boundaries may the same for each identified object,” tracking boundary is understood to be the same as the calimed segmentation) to a plurality of 3D-tomographic image data of an examination object, ([0023] “The object detector 117 may generate an indication of the position of each identified object in each image frame” [0038] “the object detection and motion scores may be performed on three-dimensional volume data.”) and wherein the segmentation algorithm is configured to segment at least one organ ([0026] “ tracking boundary and hence the identified object (e.g., organ)”) in the plurality of 3D-tomographic image data to which the segmentation algorithm is applied, ([0019] “object detector 117 may analyze each image frame acquired with the ultrasound imaging system 100 and identify anatomical features within each image frame, such as a heart, liver, lungs, blood vessels, and/or other organs, tissue, and/or structure.” )
apply a scoring function to the at least one segmented organ, wherein the scoring function is configured to determine at least one scoring value for the at least one segmented organ to which the scoring function is applied, ([0025] “determine a motion score based on a difference between the position of the tracking boundary in the second image frame and the position of the tracking boundary in the first image frame.” And [0026] “The motion score represents the change in position of the tracking boundary and hence the identified object (e.g., organ)”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Cherkezyan to have applying a segmentation algorithm to segment an organ and apply a scoring function to determine if it contains an artifact as taught by Tsymbalenko to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been so that (Tsymbalenko et al [0011] “image artifacts may be reduced in a manner that is most appropriate for the individual objects being tracked.”)
Allowable Subject Matter
Claims 4, 6-8 and 14-19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Cherkezyan et al US20230145920 discloses an inconsistency metric to determine if there is motion within the 3D tomographic imagery but does not render obvious the claimed combination as a whole.
Tsymbalenko et al US20200121294 discloses applying a tracking boundary to segment an organ to determine a motion score of the object to quantify how much motion artifact is contained in the pixels of the object
Fonte et al US20220277446 discloses segmenting a portion of a vessel and evaluating a quality score based on the segmentation but does not render obvious the claimed combination as a whole.
Jemaa et al US20240303822 discloses determining a contrast mismatch between the segmented organ between two frames of the tomography to remove any stack transition artifact but does not render obvious the claimed combination as a whole.
He et al NPL “Spatial-Temporal Image-Constrained Lung 4D-CT Reconstruction for Radiotherapy Planning” discloses determining a contour of an organ to remove artifacts but does not render obvious the claimed combination as a whole.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Golden et al US20250157034 teaches generating a 3D contour of the segmented organ
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/OWAIS I MEMON/Examiner, Art Unit 2663
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