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 filed 02/26/26 have been fully considered but they are not persuasive.
Regarding claim 1, Applicant states that the prior art reference of Sharma fails to teach of (b) determining, by application of a prediction model to said pre-contrast agent, such that a sequence of said pre-contrast image and a contrast image depicting said body part of the patient during injection of contrast agent in accordance with the determined candidate values of the at least one injection parameter minimizes a radiation dose (Applicants Remarks page 6). Examiner disagrees with Applicant. Applicant, in particular, states that the trigger time to initiate imaging is not an injection parameter for injecting a contrast agent (Applicants Remarks page 6).
Viz teaches Obtaining pre-contrast image
( [0074], [0902], [1102]: "A non-contrast scan is optionally performed ... to establish a baseline image for the area to be monitored before delivery of a contrast agent).
Prediction model applied to pre-contrast image
( [0075]–[0078], [0910], [1110]: Pre-contrast images (AIF/TUC signal) are input to a machine learning model (regression, neural network) to predict contrast kinetics and scan timing)
Minimizing radiation dose:
( [0087]–[0088], [0112]: Adaptive/personalized scan prescriptions minimize unnecessary scanning (and thus radiation)).
Sharma teaches In order to optimize imaging and minimize exposure of subject 118 to radiation, medical imaging system 112 should not begin imaging subject 118 until the contrast agent reaches a target region of interest of subject 118. optimization module 120 applies a computational model to determine a trigger time to initiate imaging of subject 118 by medical imaging system 112 upon injecting the contrast agent into subject 118 (paragraph 0034). In other words, the trigger time is computed and determined when the contrast agent reaches the target region of interest of subject 118 (patient), since the imaging of the subject 118 cannot begin until the contrast agent reaches, the trigger time can be read as an injection parameter, since The trigger time represents the interval of time between the start of the injection of the contrast agent and the initiation of the scan by medical imaging system 112.
Applicant states that Sharma does not disclose a model using the pre contrast image of the person receiving the contrast agent to determine parameters (Applicants Remarks page 6). Examiner disagrees with Applicant. Sharma teaches optimization module 120 applies a computational model to determine a trigger time to initiate imaging of subject 118 (paragraph 0034). imaging subject 118 (e.g., a patient) (paragraph 0032). Therefore, in Sharma pre-contrast image and a contrast image depicting said body part of the patient (subject 118) during injection of contrast agent
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) 1-5, 7, 12, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vaz et al US 2021/0133960 in view of Sharma et al US 2018/0071452.
Regarding claim 1, Vaz et al teaches a method comprising the implementation, by a data processor of a server, of steps of:
obtaining a pre-contrast image depicting a body part of a patient prior to an injection of contrast agent (a non-contrast scan is optionally performed. baseline image may then be used to align the patient and the region of interest within the imaging device (paragraph 0074 and fig 1 and 9);
Vaz et al fails to teach b) determining, by application of a prediction model to said pre-contrast image, candidate value(s) of at least one injection parameter of said injection of contrast agent, such that a sequence of said pre-contrast image and a contrast image depicting said body part of the patient during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter(s) minimizes a radiation dose
Sharma et al teaches (b) determining, by application of a prediction model to said pre-contrast image, candidate value(s) of at least one injection parameter of said injection of contrast agent, such that a sequence of said pre-contrast image and a contrast image depicting said body part of the patient during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter(s) minimizes a radiation dose (imaging subject 118 (e.g., a patient) (paragraph 0032). optimization module 120 applies a computational model to determine a trigger time (candidate value of at least one injection parameter) to initiate imaging of subject 118 (patient) by medical imaging system 112 upon injecting the contrast agent into subject 118 (paragraph 0034) In order to minimize the patient's exposure to radiation, imaging should not be started until the contrast agent reaches the target region of interest of the patient (paragraph 0003) Note: by determining the time at which injection of contrast agent should be dispensed, reduces the patients exposure to radiation.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vaz et al to include: b) determining, by application of a prediction model to said pre-contrast image, candidate value(s) of at least one injection parameter of said injection of contrast agent, such that a sequence of said pre-contrast image and a contrast image depicting said body part of the patient during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter(s) minimizes a radiation dose.
The reason of doing so would be to reduce process time and radiation exposure by calculating the time of injection and scan exposure.
Regarding claim 2, Vaz et al teaches wherein step (a) also comprises obtaining value(s) of at least one context parameter of said pre-contrast images said prediction model using said at least one context parameter as input at step (b) such that said contrast image has the same value(s) of the at least one context parameter as the pre-contrast image (the AIF signal may be entered as input to a machine learning (ML) model that may output the estimated AIF curve (context parameter) and estimated VOF curve (context parameter) Note: the AIF curve is measured prior to the scan reading on context parameter of pre-contrast image – see paragraph 0029). Based on the output of the ML model, the desired time for administration of the second contrast bolus relative to the administration of the first contrast bolus may be determined, and then the second contrast bolus may be administered at the desired time (paragraph 0027-0028 and 0062). Note: the AIF curve is measured before can and then the output of the model is used in CT scan, thus having the same values. A portion of the AIF curve may be directly measured prior to a first contrast scan (pre-contrast image) commencing or during the first portion of the first contrast scan, and this portion may be used as input to a model to estimate the remaining AIF curve and the VOF curve for the patient (paragraph 0029).
Regarding claim 3, Vaz et al teaches wherein the at least one context parameter includes one or more of a physiological parameter and an acquisition parameter (arterial inflow function (AIF) curve (acquisition parameter) and arterial inflow function signal (AIF) (physiological parameter) (paragraph 0028) Note: the AIF signal is measured from region of interest of artery, ie artery, internal carotid artery, etc (paragraph 0028), the AIF curve is acquired form the AIF signal.
Regarding claim 4, Vaz et al teaches wherein said the at least one injection parameter(s) comprises an injection duration prior to the acquisition of the contrast image (the first contrast scan may be a CTP scan and the first contrast injection may be a timing bolus (injection duration) for the CTP scan (paragraph 0080).
Regarding claim 5, Vaz et al teaches wherein said pre-contrast image is acquired by an x-ray medical imaging device connected to the server, said radiation dose being inflicted by the x-ray medical imaging device to said body part (imaging system 200 includes a control mechanism 208 to control movement of the components such as rotation of the gantry 102 and the operation of the x-ray source 104. In certain embodiments, the control mechanism 208 further includes an x-ray controller 210 configured to provide power and timing signals to the x-ray source 104 (paragraph 0049 and fig 1 and 2) Note: the control mechanism that controls x-rays source is connected to computer device, that stores the data in a storage device or mass storage 218, that reads on server.
Regarding claim 7, Vaz et al in view of Sharma et al teaches a step (c) of providing said determined candidate value(s) of the at least one injection parameter to the x-ray medical device, and obtaining in response the contrast image acquired by said x-ray medical imaging device which depicts said body part during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter (Sharma: the region of interest of the patient is caused to be imaged based on the administration characteristics for administering the contrast agent and the trigger time. For example, in one embodiment, imaging of the region of interest of the patient is automatically initiated (e.g., by optimization module 120 of FIG. 1) after the trigger time has elapsed upon administering the contrast agent to the patient in accordance with the administration parameters. In another embodiment, imaging of the region of interest of the patient is caused to be manually initiated by a user, e.g., via a notification or any other indication or instruction (e.g., from optimization module 120 (paragraph 0054 and fig 1).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vaz et al to include: a step (c) of providing said determined candidate value(s) of the at least one injection parameter to the x-ray medical device, and obtaining in response the contrast image acquired by said x-ray medical imaging device which depicts said body part during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter
The reason of doing so would be to reduce process time and radiation exposure by calculating the time of injection and scan exposure.
Regarding claim 12, Vaz et al in view of Sharma et al teach wherein said prediction model comprises a Convolutional Neural Network, CNN (Sharma et al: The machine learning approaches may include, e.g., regression, instance-based methods, regularization methods, decision tree learning, Bayesian, kernel methods, clustering methods, association rule learning, artificial neural networks, dimensionality reduction, ensemble methods, or any other suitable machine learning approach. In one embodiment, the machine learning models may be trained using methods known in the art (paragraph 0098).
Regarding claim 13, Vaz et al teaches a method comprising the implementation, by a data processor of a server (imaging system 200 includes a control mechanism 208 to control movement of the components such as rotation of the gantry 102 and the operation of the x-ray source 104. In certain embodiments, the control mechanism 208 further includes an x-ray controller 210 configured to provide power and timing signals to the x-ray source 104 (paragraph 0049 and fig 1 and 2) Note: the control mechanism that controls x-rays source is connected to computer device, that stores the data in a storage device or mass storage 218, that reads on server,
Sharma et al teaches for each of a plurality of training pre-contrast images from a base of training pre-contrast or contrast images respectively depicting a body part of a patient prior to and during an injection of contrast agent, each image being at least associated to reference value(s) of at least one injection parameter of said injection of contrast agent (imaging subject 118 (e.g., a patient) (paragraph 0032). a contrast agent may be injected into subject 118 prior to medical imaging system 112 imaging subject 118. In order to optimize imaging and minimize exposure of subject 118 to radiation, medical imaging system 112 should not begin imaging subject 118 until the contrast agent reaches a target region of interest of subject 118 (paragraph 0034), of a step of determining candidate value(s) of the at least one injection parameter(s) by application of a prediction model to said training pre-contrast image, such that the sequence of said training pre-contrast image and at least a contrast image depicting said body part of the patient during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter(s) minimizes a radiation dose (optimization module 120 applies a computational model to determine a trigger time (candidate value of at least one injection parameter) to initiate imaging of subject 118 (patient) by medical imaging system 112 upon injecting the contrast agent into subject 118. The trigger time represents the interval of time between the start of the injection of the contrast agent and the initiation of the scan by medical imaging system 112 (paragraph 0034) Optimization module 120 determines the trigger time for initiating imaging of subject 118 to reduce patient exposure to radiation and avoid the inaccuracies associated with conventional systems (paragraph 0035); and
verifying if the sequence of said training pre-contrast image and the contrast image minimizes the radiation dose (Optimization module 120 determines the trigger time for initiating imaging of subject 118 to reduce patient exposure to radiation and avoid the inaccuracies associated with conventional systems (paragraph 0035).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vaz et al to include: for each of a plurality of training pre-contrast images from a base of training pre-contrast or contrast images respectively depicting a body part prior to and during an injection of contrast agent, each image being at least associated to reference value(s) of at least one injection parameter of said injection of contrast agent, of a step of determining candidate value(s) of the at least one injection parameter(s) by application of a prediction model to said training pre-contrast image, such that the sequence of said training pre-contrast image and at least a contrast image depicting said body part during injection of contrast agent in accordance with the determined candidate value(s) of the at least one injection parameter(s) minimizes a radiation dose, verifying if the sequence of said training pre-contrast image and the contrast image minimizes the radiation dose
The reason of doing so would be to reduce process time and radiation exposure by calculating the time of injection and scan exposure.
Regarding claim 15, Vaz et al teaches A non-transitory computer-readable medium storing code instructions which, when executed by a computer, cause the computer to carry out a method according to claim 1 (computing device 216 may include the instructions in non-transitory memory (paragraph 0057).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Vaz et al US 2021/0133960 in view of Sharma et al US 2018/0071452 further in view of Lazarev et al US 20220415505.
Regarding claim 6, Vaz et al in view of Sharma et al teach all of the limtiations of claims 1 and 5
Vaz et al in view of Sharma et al fails to teach wherein said pre-contrast and contrast images are mammographies.
Lazarev et al teaches wherein said pre-contrast and contrast images are mammographies (the mammography can be performed with a source optimized for image quality and contrast (paragraph 0056)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vaz et al in view of Sharma et al to include: wherein said pre-contrast and contrast images are mammographies
The reason of doing so would be to view images of the body using a specific scan.
Allowable Subject Matter
Claims 8-11 and 14 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 should be directed to Michael Burleson whose telephone number is (571) 272-7460 and fax number is (571) 273-7460. The examiner can normally be reached Monday thru Friday from 8:00 a.m. – 4:30p.m. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at (571) 270- 3438.
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Michael Burleson
Patent Examiner
Art Unit 2683
Michael Burleson
April 4, 2026
/MICHAEL BURLESON/
/AKWASI M SARPONG/SPE, Art Unit 2681 4/6/2026.