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 .
This office action is in response to communication fled on 7/30/2026. Claims 1-13 and 15-21 are pending on this application.
Response to Arguments
Applicant’s arguments, see Remarks, filed 7/30/2026, with respect to the rejection of claims 1-13 and 15-21 under 35 USC 101 have been fully considered and are persuasive. The rejection of claims 1-13 and 15-21 under 35 USC 101 has been withdrawn.
Applicant's arguments filed 7/30/2026 with respect to the rejection of claims 1-4, 10-12, 15, 16, and 19 under 35 USC 102(a)(1) have been fully considered but they are not persuasive.
Response to Remarks
Regarding claim 1, applicant asserts that Ditt fails to disclose that the processor is configured to process the contrasted imagery to determine a location of the clot (Remarks claim 12).
Examiner respectfully disagrees.
Ditt teaches that CTA is performed to determine affected regions, assess the collateral status, and localize thrombus (para. [0003]). The output of the CTA is interpreted to be contrasted imagery. Ditt further teaches that when CT imaging data comprises CTA data, a location of a blood clot of a vessel can be determined (para. [0018]). This is interpreted to mean that the CTA data is analyzed/processed first and then used for determining the location of the blood clot. Therefore Ditt teaches the claimed limitation.
Also regarding claim 1, applicant asserts that Ditt fails to disclose determining a clot composition based on the further input imagery at the determined location (Remarks page 13).
Examiner respectfully disagrees.
Ditt teaches applying a clot composition determination algorithm to registered imaging data and the representation of the blood clot to obtain clot composition information (para. [0030]). The representation of the blood clot uses at least one non-contrasted computed tomography image as mandatory input (para. [0028]), which is interpreted to be the further input imagery, and is used to determine the location of the blood clot (para. [0029]). Also, the algorithm analyzes a shape, a density, and a texture of the blood clot to generate the clot composition information (para. [0031]). These characteristics would be at a determined location. Therefore Ditt teaches determining a clot composition based on the further input imagery at the determined location.
Claim Rejections - 35 USC § 102
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 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) 1-4, 10-12, 15, 16, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ditt et al (EP3798971A1).
Regarding claim 1, Ditt discloses a system for determining position and composition of a clot in a patient (para. [0013], The stroke information can comprise, for example, at least one of a location of the blood clot; para. [0031], In particular, the clot composition information can be based on and/or comprise a degree of calcification of the blood clot. The algorithm for generating the clot composition can be configured to analyze at least one of a shape, a density and a texture of the blood clot to generate the clot composition information that characterizes the clot composition), the system comprising:
a processor (para. [0073]) in communication with memory (para. [0075]), the processor configured to:
receive input imagery of a region of interest comprising contrasted input imagery (para. [0018], [0084], CTA volume data) and further input imagery contrasted lower than the contrasted input imagery (para. [0019], [0084], non-contrast computed tomography image);
process the contrasted imagery to determine a location of the clot (para. [0029], Based on the representation of the blood clot, the location and/or the size of the blood clot, in particular in form of a thrombus, can be determined);
spatially register the contrasted imagery and the further input imagery into a common coordinate system to map the determined location of the clot from the contrasted imagery to a corresponding location in the further input imagery (para. [0010]-[0011]);
determine a clot composition based on processing the further input imagery at the determined corresponding location (para. [0028]-[0030]; para. [0031], In particular, the clot composition information can be based on and/or comprise a degree of calcification of the blood clot. The algorithm for generating the clot composition can be configured to analyze at least one of a shape, a density and a texture of the blood clot to generate the clot composition information that characterizes the clot composition); and
provide an indication of said clot location and clot composition (para. [0087], Exemplary outputs are the first output OU1, comprising the clot location, size and composition, the second output OUT2, comprising the name of the occluded vessel, the third output OUT3, comprising a marking of the affected regions, a predicted tissue viability and an estimated onset time, and the fourth marking OUT4, comprising a visualization, for example in form of an attention map, of individual inputs’ contributions to the output of the corresponding algorithm).
Regarding claim 2, Ditt discloses a system wherein the processor implements one or more of i) a trained machine learning model (para. [0028], The algorithm for generating the representation of the blood clot can be based, for example, on an image-to-image model that maps input non-contrast CT imaging/CTA data to the representation of the blood clot and can be trained, for example, based on annotated ground truth imaging data in which one or more blood clots are segmented), and ii) a non-machine learning segmentation algorithm (para. [0053], For localization of blood clots, a classifier/detector approach may be used as well,
potentially followed by a distinct segmentation step that may or may not be machine-learning based itself).
Regarding claim 3, Ditt discloses a system wherein the processor implements a trained machine learning model (para. [0028], The algorithm for generating the representation of the blood clot can be based, for example, on an image-to-image model that maps input non-contrast CT imaging/CTA data to the representation of the blood clot and can be trained, for example, based on annotated ground truth imaging data in which one or more blood clots are segmented).
Regarding claim 4, Ditt discloses a system wherein the trained machine learning model is configured to receive only non-contrasted imagery as an input (para. [0028], The algorithm for generating the representation of the blood clot can be based, for example, on an image-to-image model that maps input non-contrast CT imaging).
Regarding claim 10, Ditt discloses an imaging arrangement comprising the system of claim 1 and an imaging apparatus configured to provide at least one of the contrasted input imagery and the lower-contrasted input imagery (para. [0069]).
Regarding claim 11, Ditt discloses an imaging arrangement of claim 10, wherein the imaging apparatus is configured for X-ray based tomographic imaging (para. [0069]).
Regarding claim 12, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 19, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
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.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ditt et al (EP3798971A1) in view of Herrmann et al (US20210236080).
Regarding claim 5, Ditt fails to teach a system wherein the contrasted imagery and the lower contrasted imagery comprises reconstructed imagery in an image domain.
However Herrmann teaches wherein contrasted imagery (para. [0039], CTA images) and lower contrasted imagery (para. [0122], NCCT) comprises reconstructed imagery in an image domain (para. [0039], a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain).
Therefore taking the combined teachings of Ditt and Herrmann as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Herrmann into the system of Ditt. The motivation to combine Herrmann and Ditt would be to facilitate automated localization of large vessel occlusions (para. [0003] of Herrmann).
Claim(s) 6, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ditt et al (EP3798971A1) in view of Berger et al (WO2021008697A1) and Graumann et al (US6317621).
Regarding claim 6, Ditt fails to teach a system wherein the contrasted imagery comprises one or more 2D angiograms in projection domain, and wherein the processor is further configured to perform a back-projection of an output received from the trained machine learning model into an image domain to identify a 3D voxel location that corresponds in the image domain to a determined location of the clot in the projection domain.
Berger teaches wherein the contrasted imagery comprises one or more 2D angiograms in projection domain (para. [0006], [0026], [0033]).
Therefore taking the combined teachings of Ditt and Berger as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Berger into the system of Ditt. The motivation to combine Berger and Ditt would be to provide a better understanding of the structure of a vessel (para. [0019] of Berger).
Graumann teaches performing a back-projection of an output received from the trained machine learning model into an image domain to identify a 3D voxel location that corresponds in the image domain (col. 2 lines 17-28, the angiographic 3D image would comprise voxels). It would be obvious to apply the steps to modified Ditt to determined location of the clot.
Therefore taking the combined teachings of modified Ditt and Berger as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Berger into the system of modified Ditt. The motivation to combine Berger and modified Ditt would be to reduce radiation exposure during intervention (col. 2 lines 10-16 of Graumann).
Regarding claim 17, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Claim(s) 7, 18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ditt et al (EP3798971A1) in view of Langan et al (US20170039734).
Regarding claim 7, Ditt fails to teach a system wherein the further input imagery includes a time series of frames, and the processor is configured to determine the clot composition based on an over-time contrast uptake as per the time series.
However Langan teaches input imagery includes a time series of frames (para. [0025], It should be appreciated that aspects of the present approach, such as those related to temporal and/or spatial data aggregation as well as the differential acquisition of data points over time; para. [0052]), and a processor (para. [0035]) configured to determine a clot (para. [0050]) based on an over-time contrast uptake as per the time series (para. [0023]).
Therefore taking the combined teachings of Ditt and Langan as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Langan into the system of Ditt. The motivation to combine Langan and Ditt would be to achieve high frame rate, three-dimensional imaging (para. [0005] of Langan).
Regarding claim 18, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Regarding claim 21, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ditt et al (EP3798971A1) in view of van Walsum et al (US20200222018).
Regarding claim 8, Ditt fails to teach a system wherein the indication of the clot location is determined in relation to a further 2D image.
However van Walsum teaches wherein an indication of a location is determined in relation to a further 2D image (para. [0023], [0111]).
Therefore taking the combined teachings of Ditt and van Walsum as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of van Walsum into the system of Ditt. The motivation to combine van Walsum and Ditt would be to reduce the exposure of patients to the contrast agent as well as exposure to X-ray radiation (para. [0008] of van Walsum).
Regarding claim 9, the modified system of Ditt teaches a system wherein the further 2D image is a live X-ray image (para. [0035], [0104] of Van Walsum), and wherein the processor is further configured to overlay a graphic component (para. [0104] of van Walsum) for at least one of the clot location (para. [0029] of Ditt) and clot composition information (para. [0029] of Ditt) on the live X-ray image (para. [0104] of van Walsum). It would be obvious to acquire the image during a procedure (para. [0004] of van Walsum) including a thrombectomy.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ditt et al (EP3798971A1) in view of Comaniciu et al (US10282588).
Regarding claim 13, Ditt teaches a computer implemented method further comprising training data suitable for training a machine learning to predict clot location (para. [0053], For localization of blood clots, a classifier/detector approach may be used as well, potentially followed by a distinct segmentation step that may or may not be machine-learning based itself; para. [0056]-[0058]).
Ditt fails to teach modifying existing brain imagery to simulate locations of a virtual clot. However Comaniciu teaches modifying existing brain imagery (col. 11 lines 9-17) to simulate locations of a virtual object (col. 4 lines 31-37, col. 13 lines 20-23, it would be obvious to replace the tumors with a different object such as a clot).
Therefore taking the combined teachings of Ditt and Comaniciu as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Comaniciu into the system of Ditt. The motivation to combine Comaniciu and Ditt would be to assist physicians and patients, provide guidance or recommendations, and/or provide a second pathological review (col. 4 lines 37-39 of Comaniciu).
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 LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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, Gregory Morse can be reached at 571-272-3838. 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.
/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
Prosecution Insights