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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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) 1-8, 11-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US2020/0226422) in view of Tu et al. (US2023/0230231).
To claim 1, Li teach a method, comprising:
receiving, at a processor, a series of intravascular ultrasound (IVUS) images of a vessel of a patient, the series of IVUS images comprising a plurality of frames (abstract, paragraphs 0003, 0074, IVUS imaging);
identifying, by the processor via a first machine learning (ML) model, a set of frames of the plurality of image frames, wherein frames of the set of frames are associated with one or more side branches of the vessel; identifying, by the processor via a second ML model, a location of at least one of the one or more side branches in one or more frames of the plurality of image frames; selecting, by the processor, a subset of frames from the set of frames based in part on output from the first ML model and output from the second ML model (paragraph 0029, the tissue type or tissue characteristic, region of interest (ROI), feature of interest, classes or types or blood vessel feature selected for segmentation and/or detection and representation in one or more mask, images, or outputs includes tissue maps; paragraphs 0103, 0113, 0241, image interpretation training Automatic detection and measurement of the EEL diameter addresses these technical challenges faced when diagnosis or otherwise evaluating a patient for treatment options; paragraph 0104, region containing a side branch or a stent strut. Each region/feature corresponding to lumen L, intima I, plaque Q, adventitia ADV, imaging probe P, media M, and others may be generated by the MLS using a trained NN such as a CNN… each feature or class identified, such as ADV, EEL, IEL, L, P, I, Q may be generated as a mask or a predictive mask using one or more of the trained neural networks; paragraph 0194, two neural networks are used, such that a first neural network is used for lumen detection and a second neural network is used to detect other arterial features after lumen detection has been performed).
But, Li do not expressly disclose for side branch association, said first ML model trained as a frame-level classifier, second ML model applied to the set of frames identified by the first ML model.
However, it would have been obvious in view of paragraph 0194 of Li in using sequential processing of different neural networks, as side branch targeted design preference.
Tu teach a method having a first neural network trained to identify frames associated with one or more side branches of the vessel; and a second neural network trained to identify side branches from the output of the first neural network (Fig. 9, paragraphs 0105-0106, identify main branch, then identify side branch from identified main branch).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Tu into the method of Li, in order to further implementation in side branch identification.
To claim 12, Li and Tu teach an apparatus for an intravascular imaging device (as explained in response to claim 1 above).
To claim 19, Li and Tu teach at least one machine readable storage device, comprising a plurality of instructions that in response to being executed by a processor of an intravascular ultrasound (IVUS) imaging system (as explained in response to claim 1 above).
To claims 2 and 13, Li and Tu teach claims 1 and 12.
Li teach wherein the first ML model is trained to infer the set of frames from the plurality of image frames (paragraph 0024).
To claims 3 and 14, Li and Tu teach claims 1 and 12.
Li teach wherein the first ML model is configured to receive as input one or more adjacent frames from the plurality of image frames (paragraph 0024).
To claims 4 and 15, Li and Tu teach claims 1 and 12.
Li teach wherein the first ML model is a convolutional neural network (CNN), a vision transformer network, or a combination of CNN and vision transformer networks (paragraph 0194, CNN).
To claims 5 and 16, Li and Tu teach claims 4 and 15.
Li teach wherein the first ML model is configured apply a convolution window over a single frame or multiple adjacent frames from the plurality of image frames until all frames of the plurality of image frames have been received as input (paragraph 0024).
To claims 6 and 17, Li and Tu teach claims 1 and 12.
Li teach wherein the second ML model is trained to: determine, for each frame of the plurality of image frames, whether the frame represents a side branch if the one or more side branches; and
identify, for each frame determined to represent the side branch of the one or more side branches, the location in the frame of the side branch of the one or more side branches (paragraphs 0104, 0136, 0241).
To claims 7 and 18, Li and Tu teach claims 1 and 12.
Li teach wherein the first ML model is configured to output, for each frame of the set of frames, a confidence score representing a confidence in the detection of the one or more side branches in the frame and wherein selecting a subset of frames from the set of frames comprises:
identifying frames from the set of frames with a confidence score greater than or equal to a threshold level; and selecting the identified frames for inclusion in the subset of frames (obvious in paragraph 0100, ability to quickly and automatically obtain one or more scores associated with a given plaque or stenosis to help facilitate decision making by an end user; paragraphs 0117, 0242, measurements can be used to generate various ratings or scores suitable for consideration by end users; paragraph 0125, probability maps are assessed using a scoring or weighting system by which the output probability maps are compared for each frame of image data and used to validate which pixels have a higher relative probability of being one of the classes).
To claim 8, Li and Tu teach claim 1.
Li teach wherein the second ML model is trained to generate an indication of the location as a bounding box (Figs. 17-18; paragraphs 0204, 0230, lumen contour or border is flattened to emphasize meaningful ROI/FOI data).
To claim 11, Li and Tu teach claim 1.
Li teach wherein the second ML model is a convolutional neural network (CNN), a vision transformer network, or a combination of CNN and vision transformer networks (paragraph 0194, CNN).
Claim(s) 9-10, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US2020/0226422) in view of Tu et al. (US2023/0230231) and Elbasiony (US8831321).
To claims 9 and 20, Li and Tu teach claims 8 and 19.
But, Li and Tu do not expressly disclose wherein selecting frames from the ones of the set of frames of the series of IVUS images comprises: identifying adjacent frames from the plurality of image frames where the bounding boxes in each frame are within a threshold distance from each other; and merging the side branches associated with the identified frames.
However, Li does teach probability maps and tissue maps may be combined, compared, convolved, and otherwise used to generate output results of classifying regions and features of interest using a trained neural network (paragraphs 0026, 0236).
Elbasiony teach side branch detection methods, wherein identifying adjacent frames from the plurality of image frames where the bounding boxes in each frame are within a threshold distance from each other; and merging the side branches associated with the identified frames (Figs. 6-8; column 2 lines 47-67, column 12 lines 46-54, column 13 lines 34-53), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Li and Tu, in order to build combined presentation for analysis.
To claim 10, Li, Tu and Elbasiony teach claim 9.
Li, Tu and Elbasiony teach wherein the first ML model is configured to output, for each frame of the set of frames, a confidence score representing a confidence in the detection of the one or more side branches in the frame (Li, paragraph 0100, ability to quickly and automatically obtain one or more scores associated with a given plaque or stenosis to help facilitate decision making by an end user; paragraphs 0117, 0242, measurements can be used to generate various ratings or scores suitable for consideration by end users; paragraph 0125, probability maps are assessed using a scoring or weighting system by which the output probability maps are compared for each frame of image data and used to validate which pixels have a higher relative probability of being one of the classes) and wherein merging the side branches associated with the identified adjacent frames comprises: identifying the one of the adjacent frames from the set of frames where the bounding boxes are within a threshold distance of each other with the highest confidence score; and selecting the identified one of the adjacent frames with the highest confidence score as the frame from the plurality of image frames for inclusion in the subset of frames (obvious as explained in teachings of Elbasiony, wherein selecting identified one with the highest confidence score would be obvious for one of ordinary skill in the art to incorporate for implementation, which is well-known for ensuring accuracy in practice in the art, hence Official Notice is also taken).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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, Stephen R Koziol can be reached at (408) 918-7630. 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.
ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 August 31, 2026
Prosecution Insights