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 .
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 12-13 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.
In claim 12, “the segmentation algorithm” lacks antecedent basis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-10, 14-16, 19, 23-25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wagner et al. (US 2020/0410666; hereinafter Wagner).
Wagner shows a method for vascular tracking ([0039]), comprising: determining a plurality of first vascular movements and a plurality of first background movements of an object based on a reference image and a plurality of contrasted images of the object, wherein the reference image and the plurality of contrasted images are images of blood vessels within the object which provides enhanced visualization at a region of interest ([0055]), the plurality of first vascular movements represent vascular movements in the plurality of contrasted images relative to the reference image (vasculature motion model; [0068]-[0074]), and the plurality of first background movements represent background movements in the plurality of contrasted images relative to the reference image (respiratory motion model; [0075]-[0082]); determining a movement determination model based on the plurality of first vascular movements and the plurality of first background movements, wherein the movement determination model characterizes a vascular motion of a vascular region between at least two frames of the plurality of contrasted images, a background motion of a background region between the at least two frames of the plurality of contrasted images, or a relationship between the vascular motion and the background motion (vascular motion model and respiratory motion model; [0083]); and determining a predictive vascular region in a target image based on the movement determination model, wherein the target image is a non-contrasted image of the object ([0067]-[0068], [0074], [0084]).
Wagner shows a method for surgical path planning ([0003], [0091]), providing enhanced visualization of a predictive vascular region in the non-contrasted image ([0039], [0056], [0059], [0091], [0112], [0116]-[0118]); and generating a planning path for a vascular interventional surgery based on the predictive vascular region in the non-contrasted image ([0039], [0056], [0059], [0091], [0112], [0116]-[0118]). Wagner shows a method for vascular tracking ([0039]), comprising: obtaining a plurality of contrasted images of an object, each of the plurality of contrasted images including a vascular region and a background region ([0055]); determining a movement determination model based on a plurality of vascular regions (vasculature motion model; [0068]-[0074]) and a plurality of background regions of the plurality of contrasted images (respiratory motion model; [0075]-[0082]), wherein the movement determination model characterizes a vascular region movement between different contrasted images, a background region movement between different contrasted images, or a relationship between the vascular region movement and the background region movement (vascular motion model and respiratory motion model; [0083]); and determining a predictive vascular region in a non-contrasted image of the object based on the movement determination model ([0067]-[0068], [0074], [0084]).
Wagner also shows wherein the plurality of contrasted images include at least one of an X-ray image, a computed tomography (CT) image, or a magnetic resonance imaging (MRI) image ([0043]); wherein the determining the plurality of first vascular movements based on the reference image and the plurality of contrasted images of the object comprises: determining a first vascular region in the reference image and a plurality of second vascular regions in the plurality of contrasted images ([0068]-[0074]; [0123]); and determining the plurality of first vascular movements based on the first vascular region and the plurality of second vascular regions ([0068]-[0074]; [0123]); wherein the determining the plurality of first background movements based on the reference image and the plurality of contrasted images of the object comprises: determining a first background region in the reference image and a plurality of second background regions in the plurality of contrasted images ([0075]-[0082]; [0124]); and determining the plurality of first background movements based on the first background region and the plurality of second background regions ([0075]-[0082]; [0124]); wherein the determining the plurality of first vascular movements based on the reference image and the plurality of contrasted images of the object comprises: determining a first vascular region in the reference image by processing the reference image ([0068]-[0074]; [0123]); and determining the plurality of first vascular movements based on the first vascular region and the plurality of contrasted images ([0068]-[0074]; [0123]); wherein the determining the plurality of first background movements based on the reference image and the plurality of contrasted images of the object comprises: determining a first background region in the reference image by processing the reference image ([0075]-[0082]; [0124]);; and determining the plurality of first background movements based on the first background region and the plurality of contrasted images ([0075]-[0082]; [0124]); wherein the determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises: determining, based on the plurality of first vascular movements, the plurality of first background movements, and a predetermined relational model, structural parameters of the movement determination model ([0084]); wherein the determining the predictive vascular region in the target image based on the movement determination model comprises: determining a second background movement based on the target image and a first background region of the reference image ([0075]-[0082]; [0124]); determining a second vascular movement based on the second background movement and the movement determination model ([0068]-[0074]; [0123]); and determining the predictive vascular region based on a first vascular region of the reference image and the second vascular movement ([0084]); wherein the movement determination model is a trained machine learning model ([0093]-[0095]), and the determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises: obtaining a plurality of training samples and a plurality of labels, wherein the plurality of training samples include the plurality of first background movements, and the plurality of labels corresponding to the plurality of training samples includes the plurality of first vascular movements ([0096]-[0097]); training an initial movement determination model based on the plurality of training samples and the plurality of labels ([0096]-[0097]); and obtaining structural parameters of the movement determination model until a trained movement determination model satisfies a predetermined condition ([0098], [0104]-[0105]); wherein there are multiple target images, and the method further includes: extracting the multiple target images to form an image sequence based on a target video ([0055]-[0056], [0059], [0109]); and determining the predictive vascular region based on the image sequence ([0055]-[0056], [0059], [0109]); wherein the determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises: determining structural parameters of the movement determination model based on the plurality of first vascular movements, the plurality of first background movements, and a structural parameter prediction model, the structural parameter prediction model being a trained machine learning model ([0096]-[0098], [0103]-[0104]); wherein the determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises: obtaining a vascular composite movement feature based on the plurality of first vascular movements ([0068]-[0074]; [0123]); obtaining a background composite movement feature based on the plurality of first background movements ([0075]-[0082]; [0124]); and determining structural parameters of the movement determination model based on the vascular composite movement feature and background composite movement feature ([0084], [0131]); wherein the first vascular movement is represented as a matrix, wherein each element of the matrix represents each pixel in the vascular region of the contrasted image and a movement of the each pixel along an x-direction and/or a y-direction in the contrasted image with respect to corresponding pixel in the vascular region of the reference image ([0073], [0128]-[0129], [0134]-[0137]).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wagner et al. (US 2020/0410666; hereinafter Wagner) in view of Goldfain (US 2016/0174099).
Wagner shows wherein the determining the first background region in the reference image and the plurality of second background regions in the plurality of contrasted images based on the reference image and the plurality of contrasted images through the segmentation algorithm respectively comprises: obtaining a second reference image and a plurality of second contrasted images, the second reference image being a processed reference image and each of the second contrasted images being a processed contrasted image ([0055]-[0056], [0060], [0102]); and determining the first background region and the plurality of second background regions based on the second reference image and the plurality of second contrasted images ([0055], [0060], [0102]); wherein there is at least one target image extracted based on a target video, and the method further includes: determining at least one predictive vascular region of the at least one target image and the movement determination model, the movement determination model being a trained machine learning model ([0097]).
Wagner fails to show wherein the determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises: obtaining a confidence level by performing a time-domain filtering on the plurality of first vascular movements, wherein the confidence level denotes a reliability degree of a movement of a vascular point in one of the plurality of contrasted images; and determining structural parameters of the movement determination model based on the plurality of first vascular movements, the plurality of first background movements, and the confidence level.
Goldfain discloses systems and methods for monitoring the health of an animal. Goldfain teaches obtaining a confidence level by performing a time-domain filtering ([0259]-[0261], [0270]-[0271]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wagner including determining the movement determination model based on the plurality of first vascular movements and the plurality of first background movements comprises determining structural parameters of the movement determination model based on the plurality of first vascular movements, the plurality of first background movements (Wagner, [0084]), to have further utilized confidence metrics as taught by Goldfain, as Goldfain teaches that confidence metrics may act to serve as a limiter on unnecessary or unuseful preprocessing of the data, such that unnecessary data may be discarded and further processing of the data may be halted or reduced in priority (Goldfain, [0270]), and in order to improve accuracy and quality of reported measurements (Goldfain, [0259]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Abe (US 2016/0296195) describes obtaining contrast images and determining vascular motion models ([0057]);
Nair (US 2023/0196572) describes the use of confidence values for filtering out data ([0120]);
Rakhshanfar (US 2017/0084007) describes time-domain filtering to improve reliability (abstract);
Zur (US 2018/0253839) describes time domain filtering for changing level of confidence values ([0154]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN CWERN whose telephone number is (571)270-1560. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Koharski can be reached at (571) 272-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN CWERN/ Primary Examiner, Art Unit 3797
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