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 1-20 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.
The term “high uncertainty location” in claims 1, 2, 4, 9, 11, 12, 14, and 19 is a relative term which renders the claim indefinite. The term “high uncertainty location” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term is described in the specification at section 0064 as a location associated with a level of uncertainty that is above the average level of uncertainty, but then also described as a location that has higher level of uncertainty than 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of locations of a 3D model. Given that the second description would include locations with far less uncertainty than average, it is unclear what exactly would differentiate a “high uncertainty location” from one with less uncertainty. One skilled in the art would not be able to interpret clear bounds for the term given applicant’s disclosure.
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.
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.
Claims 1, 6-11, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xia (U.S. Publication 2023/0284997) in view of Altmann (U.S. Publication 2012/0165667) and Klingensmith (U.S. Publication 2022/0370033).
As to claim 1, Xia discloses a method of generating a three-dimensional (3D) model (p. 2-3, section 0032; a CT scan produces a 3D scan model of a body part) of cardiac anatomy (p. 4, section 0043; a heart can be the part scanned), the method comprising:
capture a first set of images as a function of cardiac anatomy of the heart of the subject; using at least a processor, receiving the first set images of the cardiac anatomy (p. 2, section 0032-p. 3, section 0034; a set of slice images is captured/received in a first preliminary scan);
using at least a processor, generating a first 3D model of the cardiac anatomy as a function of the first set of images (p. 6, sections 0060-0062; the images are reconstructed in a volume rendering which reads on a 3D model);
using at least a processor, calculating a level of uncertainty at a plurality of locations on the first 3D model, wherein the plurality of locations comprises a high uncertainty region (p. 3, section 0036-p. 4, section 0040; an uncertainty map is generated using levels of uncertainty based on noise in the 3D scan volume; as best understood, any region with uncertainty larger than other regions can read on a “high uncertainty region”);
capture a second set of images as a function of the cardiac anatomy corresponding to the high uncertainty region on the first 3D model (p. 2-3, section 0032; p. 3, section 0036-p. 4, section 0038; p. 4, section 0043; a main scan is performed with modified parameters to improve images in the uncertainty regions, new images from all regions are captured and received, which would include the uncertainty regions);
and using at least a processor, generating a second 3D model as a function of the second set of images (p. 6, sections 0057-0064; after the optimized scan parameters are implemented, further images are taken and volume rendering is performed, which would generate a 3D model).
Xia does not disclose, but Altmann discloses wherein two-dimensional (2D) intracardiac echocardiogram images are captured using an intracardiac echocardiography catheter positioned within a heart of a subject (p. 2, sections 0022-0026; p. 3, section 0043; to capture 2D ultrasound images, an ultrasound catheter is inserted into a chamber of the heart, making it intracardiac; a heart ultrasound is another name for an echocardiogram). The motivation for this is to provide an easy and reliable way to create annular lesions in the tissue, as well as sensing signals along annular paths (p. 1-2, section 0021). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Xia to use two-dimensional (2D) intracardiac echocardiogram images captured using an intracardiac echocardiography catheter positioned within a heart of a subject in order to provide an easy and reliable way to create annular lesions in the tissue, as well as sensing signals along annular paths as taught by Altmann.
Xia does not disclose, but Klingensmith discloses wherein generating the first 3D model of the cardiac anatomy comprises inputting into at least a trained neural network the first set of images; and generating, using the at least a trained neural network, the first 3D model of the cardiac anatomy as a function of the first set of images (p. 3, section 0051; p. 3, sections 0059-0079; p. 7, section 0112-p. 8, section 0115; p. 12, section 0138; p. 19, section 0182; a 3D model is generated using the neural network based on calculated parameters after training the network). The motivation for this is to help classify and identify fatty tissue that is part of the heart (p. 1, section 0006). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Xia and Altmann to input into at least a trained neural network the first set of images and generate, using the at least a trained neural network, the first 3D model of the cardiac anatomy as a function of the first set of images in order to help classify and identify fatty tissue that is part of the heart as taught by Klingensmith.
As to claim 6, Altmann discloses that the captured images are 2D intracardiac echocardiogram images as noted in the rejection to claim 1. Xia in view of Altmann does not disclose, but Klingensmith discloses wherein:
generating the first 3D model using a trained neural network comprises generating a set of shape parameters based on the first set of images (p. 3, section 0051; p. 3, sections 0059-0061; p. 4, section 0077; p. 12, section 0138; p. 19, section 0182; p. 7, section 0112-p. 8, section 0115; an algorithm generates the 3D model by fitting splines and performing various transformations; the result is a set of shape parameters such as vertices and triangle faces, as well as a set of matrices based on alignment with a segmented model that can be used to generate the 3D model);
generating the set of shape parameters comprises:
receiving cardiac geometry training data comprising a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs; training a shape identification model using the cardiac geometry training data (p. 3, section 0051; p. 3, sections 0059-0061; p. 4, section 0077; p. 12, section 0138; p. 19, section 0182; p. 7, section 0112-p. 8, section 0115; the neural network is trained with received labeled image data corresponding to the segmentation into particular shapes of the heart);
inputting the first set of images into the shape identification model; and generating the set of shape parameters using the shape identification model as a function of the first set of images; and the first 3D model is generated based on the set of shape parameters (p. 3, section 0051; p. 3, sections 0059-0061; p. 4, section 0077; p. 12, section 0138; p. 19, section 0182; p. 7, section 0112-p. 8, section 0115; heart images are input and a segmented model is generated based on calculated parameters after training the network). Motivation for the combination of references is given in the rejection to claim 1.
As to claim 7, Xia discloses the use of computed tomography (p. 2, section 0031) for imagery. Further, Klingensmith discloses wherein the plurality of shape parameter sets of the cardiac geometry training data is generated using computed tomography (p. 2, section 0047-0048; p. 3, section 0055-p. 4, section 0079; the shape parameters for training the model are obtained using CT, i.e. computed tomography, imaging). Motivation for the combination of references is given in the rejection to claim 1.
As to claim 8, Xia does not disclose, but Klingensmith discloses wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range (p. 9, section 0123-p. 10, section 0124; p. 11, section 0131-p. 12, section 0134; shapes representing features such as fat, muscle, or blood, are classified into parameter sets; this occurs by sorting into nodes on a tree, each of which would encompass a range of parameters in a parameter space). Motivation for the combination of references is given in the rejection to claim 1.
As to claim 9, Xia discloses wherein the high uncertainty region is determined using model output uncertainty (p. 4, section 0040; the neural network model outputs uncertainty values that produce a complete uncertainty map, which would identify low and high uncertainty regions).
As to claim 10, Xia discloses a method further comprising displaying the second 3D model to a user (p. 7, section 0066; the reconstructed images, which would include 1st and 2nd model images, are displayed to the user).
As to claim 11, see the rejection to claim 1.
As to claim 16, see the rejection to claim 6.
As to claim 17, see the rejection to claim 7.
As to claim 18, see the rejection to claim 8.
As to claim 19, see the rejection to claim 9.
As to claim 20, see the rejection to claim 10.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Xia in view of Altmann and Klingensmith and further in view of Jiang (U.S. Publication 2024/0115242).
As to claim 2, Xia discloses wherein capturing a second set of images comprises using a display device in communication with the processor, displaying the first 3D model of the cardiac anatomy to a user (p. 7, section 0066). Further, Altmann discloses that the device for imaging is an intracardiac echocardiogram catheter, as discussed in the rejection to claim 1. Xia does not disclose, but Jiang discloses by the user, positioning the device within the heart of the subject for capturing an image of at least a portion of the cardiac anatomy corresponding to the high uncertainty region of the first model (p. 8, sections 0071-0073; the display of the model along with confidence/uncertainty values allows the user to better guide the position of the image capturing probe). The motivation for this is to obtain a more satisfactory scanning result. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Xia, Altmann, and Klingensmith to position the device within the heart of the subject for capturing an image of at least a portion of the cardiac anatomy corresponding to the high uncertainty region of the first model in order to obtain a more satisfactory scanning result as taught by Jiang.
As to claim 12, see the rejection to claim 2.
Claims 3-5 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Xia in view of Altmann, Klingensmith, and Jiang and further in view of Govari (U.S. Publication 2022/0211314).
As to claim 3, Xia discloses wherein displaying the first 3D model of the cardiac anatomy to the user comprises:
using a display device, displaying the first 3D model of the cardiac anatomy to a user (p. 7, section 0066);
generating a first map that maps the level of uncertainty at the plurality of locations to the plurality of locations on the first 3D model (p. 3-4, section 0039; a magnitude of a value is used to represent uncertainty on the map; the number would identify a high or low uncertainty pixels and associated regions).
Xia does not disclose, but Govari does disclose overlaying the first map onto the first 3D model (fig. 2a; fig. 2b; p. 2, section 0024; p. 3, sections 0033-0034; p. 3, section 0042-p. 4, section 0053; a map indicating confidence of values is overlaid onto those values on a 3D model of a heart; the map can be color-coded or shape-coded). The motivation for this is that prior art systems do not allow for the indication of quality of the values displayed in the model (p. 2, sections 0024-0026). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Xia, Altmann, Klingensmith, and Jiang to overlay the first map onto the first 3D model in order to allow for the indication of quality of the values displayed in the model as taught by Govari
As to claim 4, Xia discloses wherein the first map identifies the high uncertainty region of the first 3D model (p. 3-4, section 0039; a magnitude of a value is used to represent uncertainty on the map; the number would identify a high or low uncertainty pixels and associated regions).
As to claim 5, Xia discloses that the map is a heat map (p. 3-4, section 0039). Xia does not disclose, but Govari does disclose wherein the first map comprises a color-coded map configured to correlate at least a color to uncertainty (p. 3, section 0042-p. 4, section 0053; a map indicating confidence of values is overlaid onto those values on a 3D model of a heart; the map can be color-coded representing different numerical confidence values with different colors). Motivation for the combination of references is given in the rejection to claim 3.
As to claim 13, see the rejection to claim 3.
As to claim 14, see the rejection to claim 4.
As to claim 15, see the rejection to claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON M RICHER whose telephone number is (571)272-7790. The examiner can normally be reached 9AM-5PM.
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, King Poon can be reached at (571)272-7440. 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.
/AARON M RICHER/Primary Examiner, Art Unit 2617
Prosecution Insights