CTNF 18/719,545 CTNF 100039 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: “A COMPUTER IMPLEMENTED METHOD FOR PREDICTING CARDIOVASCULAR CONDITION ONSET” Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-5, 8-12 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipate by Bello (US 20210350179 A1) . Regarding claim 1 , Bello discloses a computer implemented method of identifying changes in a subject's heart or an adjacent region over time ([0008] ML model to receive as input a time-resolved 3D model of a heart and to output a predicted time-to-event for a cardiac event) , the method comprising: receiving a set of imaging data relating to a subject's heart, the set of imaging data comprised of images of the subject's heart obtained at a plurality of points in time (Fig. 1, [0130] obtaining image data of a subject's heart or a portion thereof, wherein the image data 23 may take the form of a sequence of images corresponding to different time points) ; generating an anatomical model of the subject's heart for a plurality of the images in the set of imaging data so as to provide a set of anatomical models of the subject's heart corresponding to a plurality of points in time (Fig. 1, [0125] time resolved 3D models may be generated from original image data (see also [0135]) ) ; aligning a plurality of the anatomical models in the set of anatomical models relative to one another so as to provide a set of aligned data ([0087] the vertices of the time-resolved 3D models are mapped to features of the subject's heart to ensure that the same vertex corresponds to the same portion of the subject's heart at each time of the time-resolved 3D models) ; identifying changes in at least one region of the subject's heart by comparing data in the set of aligned data using the machine learning model ([0181] tracking vertex trajectory across frames of the time-resolved data, e.g. Fig. 3B) ; and generating an output that is a prediction relating to an onset of a cardiovascular condition ([0093] an output in the form of a predicted time-to-event of an adverse cardiac event, or a measure of risk for an adverse cardiac event, is determined) . Regarding claim 2 , Bello discloses the method according to claim 1 as applied above. Bello further discloses wherein the steps of aligning a plurality of the anatomical models in the set of anatomical models and identifying changes in at least one region of the subject's heart by comparing the data in the set of aligned data using a machine learning model comprises: extracting data relating to the at least one region of the subject's heart from each of a plurality of the anatomical models; generating a graph representative of the extracted data for each of the plurality of anatomical models; and comparing the graphs for each of the plurality of anatomical models (Fig. 3A, 3B; [0147] extracting information of a region of a heart across timepoints via automatic cardiac image segmentation (i.e. to compare across time points, see also [0148]-[0149], [0225]); [0181] extract trajectory information of each vertex and produce a simple numerical representation of the trajectory of each vertex during a cardiac cycle as in Fig. 3B; [0081] the time resolved 3D models include information of the heart regions such as the right ventricle, left ventricle, right atrium, left atrium, myocardium, etc.) . Regarding claim 3 , Bello discloses the method according to claim 2 as applied above. Bello further discloses wherein the step of aligning a plurality of the anatomical models in the set of anatomical models relative to one another is carried out prior to extracting the data relating to the at least one region of the subject's heart such that the extracted data is extracted aligned data ([0087] the vertices are aligned; [0181] the aligned vertices are used to produce a numerical representation of the extracted data) . Regarding claim 4 , Bello discloses the method according to claim 2 as applied above. Bello further discloses wherein the step of aligning a plurality of the anatomical models in the set of anatomical models relative to one another is carried out after extracting the data relating to the at least one region of the subject's heart such that the extracted data is subsequently aligned ([0081] the time resolved 3D models include information of the heart regions such as the right ventricle, left ventricle, right atrium, left atrium, myocardium, etc.; [0087] as a result of the heart region information in the model, the vertices can be mapped to the same portion of the subject's heart as vertices in other models (i.e. aligning)) . Regarding claim 5 , Bello discloses the method according to claim 2 as applied above. Bello further discloses wherein the step of extracting data relating to the at least one region of the subject's heart from each of the plurality of the anatomical models comprises at least one of: a coordinate frame associated with the region of the subject's heart, geometric features, anatomical region codes and image intensities ([0087] the time resolved 3D models are made up of vertices in cartesian coordinates mapped to regions of the subject's heart; [0081] the time resolved 3D models may include a representation of the whole or any part of the subject's heart, such as, for example, the right ventricle, left ventricle, right atrium, left atrium, myocardium, etc.; [0148] In each image, the right ventricular wall 25, the left ventricular wall 26, the right ventricular blood pool 27 and the left ventricular blood pool 28 may be observed to have been clearly segmented; [0150] models of features such as the freewall and septum of the hearts, using vertex-wise time-resolved displacement values along x/y/z coordinates; [0125] identifying one or more anatomical boundaries and/or features of a subject's heart) . Regarding claim 8 , Bello discloses the method according to claim 1 as applied above. Bello further discloses wherein aligning a plurality of the anatomical models in the set of anatomical models relative to one another so as to provide the set of aligned data comprises: defining a coordinate frame for the plurality of the anatomical models based on at least one identifiable anatomical feature common in each of the plurality of the anatomical models; and aligning the representations by aligning the coordinate frame of each of the plurality of the anatomical models ([0087] the models are defined in Cartesian coordinates and the vertices are mapped to features of the subject's heart to ensure that the same vertex corresponds to the same portion of the subject's heart at each time of the time-resolved three-dimensional model) . Regarding claim 9 , Bello discloses the method according to claim 1 as applied above. Bello further discloses wherein the output comprises an indication of the input data or a part of the input data on which the output has been based ([0140] the outputs of the ML model include time-resolved 3D models as 3D renderings of frame-wise cardiac motion and the output data includes a predicted time-to-event of an adverse cardiac event (e.g. subject death) corresponding to the data) . Regarding claim 10 , Bello discloses the method according to claim 1 as applied above. Bello further discloses wherein the images comprise ultrasound images ([0048] wherein the data used to derive the models is ultrasound data) . Regarding claim 11 , Bello discloses everything claimed as applied above (see rejection of claim 1). Regarding claim 12 , Bello discloses the method according to claim 1 as applied above. Bello further discloses system for identifying changes in a subject's heart or an adjacent region over time, the system comprising: a memory comprising instruction data representing a set of instructions; one or more processors configured to communicate with the memory and to execute the set of instructions, wherein the set of instructions, when executed by the one or more processors ([0046] computer-readable storage medium storing a machine learning model trained according to the method; [0126] system including computer-readable storage medium) causing the one or more processors to carry out the computer implemented method . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bello (US 20210350179 A1) in view of Oliveira (US 20210125333 A1) . Regarding claim 6 , Bello discloses the method according to claim 2 as applied above. Bello further discloses wherein the machine learning model identifies changes in the at least one region of the subject's heart by comparing the graphs for each of the plurality of the anatomical models ([0147]-[0149] as in Fig. 3A, the graphs of information at different timepoints are used to track motion over time) . Bello fails to disclose wherein the changes are identified using a recurrent processing unit. Oliveira, in a related system from the same field of endeavor of temporal analysis of cardiac images of a patient (Abstract), discloses wherein changes in a model of a patient's heart over time are identified using a recurrent processing unit ([0131] recurrent neural network; [0031] the neural network stored on a processor; [0036] using the neural network to track changes across timepoints in images of the patient's heart) . It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Oliveira with Bello wherein changes in a model of a patient's heart over time are identified using a recurrent processing unit, as disclosed by Oliveira, as part of a method for identifying changes in a subject's heart or an adjacent region over time, as disclosed by Bello, for the purpose of improving reliability and reducing time needed for cardiac assessment (See Oliveira [0041]). Regarding claim 7 , Bello in view of Oliveira discloses the method according to claim 6 as applied above. Bello further discloses performing temporal processing within a cardiac cycle; and/or identifies changes of motion trajectories ([0150]-[0151] time-resolved tracking of trajectories and relative velocities of heart regions over a cardiac cycle; [0181] trajectories of vertices tracked through a cardiac cycle, including over a variety of timepoints) . Bello fails to disclose wherein this is done using a recurrent processing unit. Oliveira, in a related system from the same field of endeavor of temporal analysis of cardiac images of a patient (Abstract), discloses wherein the recurrent processing unit performs temporal processing within a cardiac cycle; and/or identifies changes of motion trajectories ([0131] recurrent neural network; [0031] the neural network stored on a processor; [0024] heart action is monitored over a time period such as a complete heart cycle; [0036] motion features are tracked of regions of the heart and determined to be normal or suspicious) . It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Oliveira with Bello wherein the recurrent processing unit performs temporal processing within a cardiac cycle; and/or identifies changes of motion trajectories, as disclosed by Oliveira, as part of a method for identifying changes in a subject's heart or an adjacent region over time, as disclosed by Bello, for the purpose of improving reliability and reducing time needed for cardiac assessment (See Oliveira [0041]) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li (US 20090180675 A1) discloses temporally aligning a plurality of cardiac image sequences including across different imaging modalities. Altmann (US 20060253031 A1) discloses registering a current ultrasound image of a patient with a previously acquired image of the patient. 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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. /CAROLINE E. DEPALMA/Examiner, Art Unit 2675 /SJ Park/Primary Examiner, Art Unit 2675 Application/Control Number: 18/719,545 Page 2 Art Unit: 2675 Application/Control Number: 18/719,545 Page 3 Art Unit: 2675 Application/Control Number: 18/719,545 Page 4 Art Unit: 2675 Application/Control Number: 18/719,545 Page 5 Art Unit: 2675 Application/Control Number: 18/719,545 Page 6 Art Unit: 2675 Application/Control Number: 18/719,545 Page 7 Art Unit: 2675 Application/Control Number: 18/719,545 Page 8 Art Unit: 2675 Application/Control Number: 18/719,545 Page 9 Art Unit: 2675 Application/Control Number: 18/719,545 Page 10 Art Unit: 2675
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