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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/9/2026 has been entered.
Response to Arguments
Applicant's arguments filed 6/9/2026 have been fully considered but they are moot in view of new grounds of rejection.
The 35 USC 101 rejection has been withdrawn.
The prior 35 USC 112 rejections are withdrawn.
Claim Objections
Claims 1,6, 14 and 15 are objected to because of the following informalities: .
Claim 1, 14 and 15 (before last limitation) recites “intensitiy”.
Claim 6 recites “inputting the identified the temporal sequence into the neural network”. The “the” should be removed.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2,5-12,14-17 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. [
Claim 1, 14 and 15 recites “inputting the identified plurality of temporal sequences of sub-region differential images representing the subset into a neural network trained to classify whether a sub-region includes a vascular constriction based on rates of change in image intensity values between successive sub-region differential images in a temporal sequence representing the sub-region;”. The Examiner is unable to find support for this limitation. The specification does not appears to disclose calculating the rate of change nor training the neural network using the rate of change. Page 6 does state “a neural network 130 trained to classify, from temporal sequences of angiographic images of the vasculature, a sub-region 120 of the vasculature as including a vascular constriction 140;” “rate of change: is shown in only (pg. 7 , 8 and 14)
PNG
media_image1.png
100
616
media_image1.png
Greyscale
PNG
media_image2.png
68
544
media_image2.png
Greyscale
PNG
media_image3.png
276
568
media_image3.png
Greyscale
Claim 1, 14 and 15 recites “identifying a temporal sequence of sub-region differential images via the neural network, based on rates of change in image intensity values in the identified temporal sequence representing a sub-region that includes the vascular constriction." The examiner is unable to find support for this limitation. The Specification does disclose identifying a subregion that includes the vascular contraction based on a classification provided by the neural network, but not based on the rate of change.
Claims 2,5-12,16-17 are rejected as dependent upon a rejected claim.
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.
Claim 1-2,5-12,14-17 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.
Claims 1, 14-15 last limitation recites “in the identified temporal sequence …” It is not clear if this refers to the 3rd limitation identifying step or the last limitation identifying step.
Claim 1, 14-15 recites “identifying, from differential images, a plurality of temporal sequences of sub-region differential images…”. The claim never generates the sub-rgion differential images, so it isn’t clear how one can identify something that has not been generated.
Claim 1, 14-15 last limitation and preamble recites “a vascular constriction”. It is not clear if the last limitation should be “the vascular constriction”.
Claim 6 recites “the identified temporal sequence”. It is not clear which oneApplicant is referring to. See claim 1 rejection above
Claim 7 recites ”determining that the represented subregion has a maximum amount of contrast agent and excluding the temporal sequence from the neural network”. It isnt’ clear what Applicant is trying to claim. Which termporal sequence is applicant referring? How does on exclude a sequence from the neural network? Do this mean not inputting the image during inference? Or training? Claim 6 says inputting the temporal sequence into the neural network, while claim 7 says the exact opposite.
Claims 2,5-12, 16-17 are rejected as dependent upon a rejected claim.
No Prior Art reads on the claims as currently written. The claims are currently rejected under 35 USC 112.
The closest Prior Art of record in addition to Zeng are Horz et al. (US 2013/0077839 A1, hereinafter "Horz") and Cong et al., "Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1301-1308 (2019) (hereinafter "Cong").
Horz discloses A computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, the method comprising:
receiving the temporal sequence of angiographic images representing a flow of a contrast agent within a vasculature; computing a temporal sequence of differential images from the temporal sequence of angiographic images, the differential images comprising a plurality of sub-regions of the vasculature; (Horz, “[0028] More specifically, the user acquires a DSA image sequence of a respective region of interest of the patient. The method 10 generates or calculates a time-contrast curve for all pixels in each input image/frame of the DSA sequence (Step 12). The user also specifies a reference parameter or fixed reference time point T for each time-contrast curve (Step 14) to be used as one of the time points for the method 10. This may be, for example, the "time-to-peak opacification" T.sub.max as described above or any other pixel-specific temporal parameter determined from the input DSA data. The value of the fixed reference time point T.sub.ref is determined for each time-contrast curve obtained from the DSA image sequence (Step 16).”; Additionally see paragraphs 4 and 7-8)
identifying, from the differential images, a plurality of temporal sequences of sub-region differential images representing a subset of the plurality of sub-regions of the vasculature (Horz ¶[0005], “ Generally, time-contrast curves are obtained or generated from a DSA image sequence by selecting a same region of interest in the DSA input images (also referred to as "frames") and measuring the average contrast density value within the region for each frame of the DSA sequence corresponding to a particular time (t).)
Horz does not expressly teach “wherein a first differential image in each temporal sequence of sub-region differential images is where the contrast agent enters the represented sub-region and a last differential image in the temporal sequence is where the contrast agent leaves the represented sub-region;”
“inputting the identified plurality of temporal sequences of sub-region differential images representing the subset into a neural network trained to classify whether a sub-region includes a vascular constriction based on rates of change in image intensitiy values between successive sub- region differential images in a temporal sequence representing the sub-region: and identifying a temporal sequence of sub-region differential images via the neural network, based on rates of change in image intensity values in the identified temporal sequence representing a sub-region that includes the vascular constriction.”
Cong discloses “wherein a first differential image in each temporal sequence of sub-region differential images is where the contrast agent enters the represented sub-region and a last differential image in the temporal sequence is where the contrast agent leaves the represented sub-region;” (Cong, Fig. 1 Step 2
PNG
media_image4.png
318
1124
media_image4.png
Greyscale
)
Cong further discloses “inputting the identified plurality of temporal sequences of sub-region differential images representing the subset into a neural network trained to classify whether a sub-region includes a vascular constriction based on rates of change in image intensitiy values between successive sub- region differential images in a temporal sequence representing the sub-region: and identifying a temporal sequence of sub-region differential images via the neural network, based on rates of change in image intensity values in the identified temporal sequence representing a sub-region that includes the vascular constriction.”(Cong, Fig. 1 Step3
PNG
media_image5.png
352
1100
media_image5.png
Greyscale
, discloses inputting each image of the temporal sequence into a CNN (see Fig. 3) and outputs Stenosis Activation map, which shows locations of stenosis)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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, Sumati Lefkowitz can be reached at 571-272-3638. 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.
/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
Prosecution Insights