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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/21/25 has been entered.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4-9, 11-13, 15 and 20-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2025/0201384; hereinafter Liu) in view of Dalvin et al. (US 2019/0239850; hereinafter Dalvin), Cadieu et al. (US 2022/0104790; hereinafter Cadieu), and Aase et al. (US 2024/0212134; hereinafter Aase).
Liu shows an ultrasound imaging system and method configured for conducting a diagnostic procedure on a subject (abstract), the system comprising: an ultrasound imaging probe ([0061], Fig. 1); a computing system ([0062]); and a non-transitory computer-readable storage medium, storing instructions that, when executed by a processor of the computing system cause the ultrasound imaging system to: acquire a plurality of ultrasound images of at least a portion of an organ of the subject, using the ultrasound imaging probe ([0065]); automatically determine whether an intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality associated a diagnostic procedure associated with the plurality of images ([0090]-[0091], [0101-[0102]); and based on determining that the intrinsic image quality is below the threshold, output an indication that: (i) use of an ultrasound enhancing agent is likely to improve the intrinsic image quality; or (ii) is not likely to improve the intrinsic image quality ([0086], [0093]-[0104]).
Liu also shows wherein the indication is provided in real time during the diagnostic procedure ([0094]-[0100]; Fig. 2); wherein the automatically determining is performed using a trained machine learning model, trained using training data comprising a plurality of data points labeled to indicate that an expert determined that the ultrasound enhancing agent was needed ([0074]-[0085], [0098]); wherein the training data comprises a plurality of data points which were captured using the ultrasound enhancing agent ([0074]-[0085], [0098]); wherein the required threshold is at least a minimum intrinsic quality required for successful completion of the diagnostic procedure ([0098], [0101]-[0104]); wherein the automatically determining comprises determining by a machine learning model, that one or more views of the organ associated with the diagnostic procedure have been captured at a probe position which would provide a clinically acceptable image quality if the intrinsic quality threshold was met and/or have been captured in a mode which would provide a clinically acceptable image quality if the intrinsic quality threshold was met, while the intrinsic image quality remains below the required threshold ([0099]-[0100]); wherein determining that one or more views of the organ associated with the diagnostic procedure have been captured at the probe position which would provide a clinically acceptable image quality if the intrinsic quality threshold was met and/or have been captured in the mode which would provide a clinically acceptable image quality if the intrinsic quality threshold was met, is performed using a machine learning model trained to determine an expected clinical quality of an acquired image based in part on the probe position and/or the acquisition mode ([0099]-[0100]); wherein the one or more views comprise a plurality of views, and an intrinsic quality of the plurality of views is integrated together to determine that the intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality associated with the diagnostic procedure ([0099]-[0100]); wherein the organ is a heart, and the acquired plurality of ultrasound images are two-dimensional ultrasound images ([0068]-[0070], [0105]-[0107]); wherein the diagnostic procedure comprises a cardiac function measurement, and the required threshold intrinsic quality associated with the diagnostic procedure is a minimum intrinsic quality needed to make the cardiac function measurement ([0105]-[0107]); wherein the cardiac function measurement comprises a measurement of ejection fraction and/or a measurement of left ventricular function ([0068]-[0070]; [0105]-[0107]); wherein the minimum intrinsic image quality comprises visibility of a minimum number of myocardial segments in the plurality of acquired ultrasound images ([0105]-[0107]); wherein processing to determine the intrinsic image quality of the plurality of ultrasound images comprises analyzing, by the machine learning model, an individual image quality of a plurality of myocardial segments of the heart of the subject ([0105]-[0107]); wherein the analyzing is performed to determine the individual image quality of the plurality of myocardial segments of the heart of the subject across multiple views ([0099]-[0100], [0105]-[0107]).
Liu fails to show determine whether the intrinsic image quality is below the threshold due to a positioning error of the ultrasound imaging probe; and based on determining that the intrinsic image quality is below the threshold, but not due to the positioning error of the ultrasound imaging probe.
Liu fails to show determine, using a machine learning model and the plurality of ultrasound images, respective image qualities of a plurality of myocardial segments of a heart of a subject; automatically determine whether an intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality based on the respective image qualities of the plurality of myocardial segments of the heart of the subject.
Liu fails to show segment the heart into a plurality of myocardial segments; track the plurality of segments; determine using a machine learning model and the plurality of images qualities of each myocardial segment of the plurality of myocardial segments of the heart of the subject based on tracking the plurality of myocardial segments. Liu also fails to show wherein the ultrasound imaging system is further configured to: determine whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality; wherein the ultrasound imaging system is further configured to: generate a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user; determining whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality; generating a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user; determine whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality; generate a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user.
Dalvin discloses systems and methods for guiding ultrasound medical exams. Dalvin teaches determine whether the intrinsic image quality is below the threshold due to a positioning error of the ultrasound imaging probe (step 404 if threshold for adequate image quality has not been met, the system enacts adjustments and updates virtual exam guidance elements; step 406 determine if detector/position/view is optimal, [0066], Fig. 4A; step 4306, the system assesses extracted features for signatures of inadequate ultrasonic transmission or contact against the examinees body, [0069], Fig. 4D); and based on determining that the intrinsic image quality is below the threshold, but not due to the positioning error of the ultrasound imaging probe (if the required anatomical structures are present in the field of view, and if the view of the target anatomical structure is optimal, then the system proceeds to step 460, [0066]; step 4318 system identifies high-probability root causes of poor image quality related to user error, [0069]).
Cadieu discloses echocardiographic techniques for determining ejection fraction. Cadieu teaches determine, using a machine learning model and the plurality of ultrasound images, respective image qualities of a plurality of myocardial segments of a heart of a subject; automatically determine whether an intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality based on the respective image qualities of the plurality of myocardial segments of the heart of the subject ([0022]-[0024]).
Aase discloses methods and systems for providing an image quality metric. Aase teaches segment the heart into a plurality of myocardial segments ([0032], [0038], [0040], [0042], [0056]); track the plurality of segments ([0032], [0038], [0040], [0042], [0056]); determine using a machine learning model and the plurality of images qualities of each myocardial segment of the plurality of myocardial segments of the heart of the subject based on tracking the plurality of myocardial segments ([0032]-[0042], [0056]). Also, wherein the ultrasound imaging system is further configured to: determine whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality (segment/track heart contours over heart cycle including from end-diastole to end-systole; [0042]); wherein the ultrasound imaging system is further configured to: generate a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user (quality processor identifies segments that may not be trackable, [0043]-[0044]; graphical identifier 512 providing on apical septal segment that is not sufficiently visible/trackable, Fig. 4); determining whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality (quality processor identifies segments that may not be trackable, [0043]-[0044]; graphical identifier 512 providing on apical septal segment that is not sufficiently visible/trackable, Fig. 4); generating a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user (quality processor identifies segments that may not be trackable, [0043]-[0044]; graphical identifier 512 providing on apical septal segment that is not sufficiently visible/trackable, Fig. 4); determine whether specific contiguous, or non-contiguous, myocardial segments of the heart of the subject are seen in the plurality of ultrasound images with a diagnostic image quality (quality processor identifies segments that may not be trackable, [0043]-[0044]; graphical identifier 512 providing on apical septal segment that is not sufficiently visible/trackable, Fig. 4); generate a contrast indication alert based on a number of non-visualized myocardial segments that is configured for a particular user (quality processor identifies segments that may not be trackable, [0043]-[0044]; graphical identifier 512 providing on apical septal segment that is not sufficiently visible/trackable, Fig. 4).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Liu to determine whether image quality is below a threshold due to a positioning error of the probe as taught by Dalvin, as Dalvin teaches that a medical ultrasound examination may produce poor quality images due to various errors including the user’s positioning of the probe, inadequate ultrasound transmission, contact against the examinee’s body, etc., thereby improving the overall accuracy of the diagnostic measurement.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Liu and Dalvin to automatically determine whether an intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality based on the respective image qualities of the plurality of myocardial segments of the heart of the subject as taught by Cadieu, in order to obtain a more accurate diagnostic measurement by using higher quality images and avoiding lower quality images, and to obtain the known benefits of automation including reducing errors caused by the user.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Liu and Dalvin, and Cadieu to segment and track myocardial segments as taught by Aase, as Aase teaches that segmentation and tracking of the heart can identify foreshadowing problems ([0042]), and as Aase teaches that tracking/segmentation provides heart contours, graphs, or points at end diastole and end systole that are compared to determine an amount of movement of an apical point depicted in the image loop of the plurality of images over a heart period ([0038]). Furthermore, Aase teaches that automated measurements and automated clinical findings may be executed by the quality assessment processor to identify images having inadequacies that may prevent subsequent automated measurements ([0034]). Furthermore, Aase teaches that the quality assessment processor may be configured to identify unsuitable image loops of ultrasound images such as non-standard images (not automatically recognized), images that cannot be automatically measured, a varying heartbeat of an image loop in comparison with at least one other image loop, images having inconsistent structural dimensions, foreshortening, non-trackable segments, and/or any suitable image problem ([0056]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2025/0201384; hereinafter Liu) in view of Dalvin et al. (US 2019/0239850; hereinafter Dalvin), Cadieu et al. (US 2022/0104790; hereinafter Cadieu), and Aase et al. (US 2024/0212134; hereinafter Aase) as applied to claim 2 above, and further in view of Sethuraman et al. (US 2024/0164756; hereinafter Sethuraman).
Liu fails to show wherein the acquired plurality of ultrasound images comprise Doppler ultrasound images; wherein the method further comprises detecting a presence or absence of a valvular pathology in the acquired plurality of ultrasound images; wherein automatically determining that the intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality associated with the diagnostic procedure comprises: estimating an expected blood flow parameter; and comparing the expected blood flow parameter with a measured blood flow parameter obtained from the Doppler ultrasound images, and the indication to the user comprises an alert that a gap between the expected parameter and the measured parameter indicates the Doppler signal is compromised; wherein the expected blood flow parameter and the measured blood flow parameter are each velocity.
Sethuraman discloses systems and methods for ultrasound acquisition feedback. Sethuraman teaches wherein the acquired plurality of ultrasound images comprise Doppler ultrasound images ([0014]-[0016], [0023]); wherein the method further comprises detecting a presence or absence of a valvular pathology in the acquired plurality of ultrasound images ([0016], [0043], [0053]-[0055]); wherein automatically determining that the intrinsic image quality of the plurality of ultrasound images is less than a required threshold intrinsic quality associated with the diagnostic procedure comprises: estimating an expected blood flow parameter; and comparing the expected blood flow parameter with a measured blood flow parameter obtained from the Doppler ultrasound images, and the indication to the user comprises an alert that a gap between the expected parameter and the measured parameter indicates the Doppler signal is compromised ([0035]-[0036], [0049]-[0051], [0054]-[0057], [0061]-[0063]); wherein the expected blood flow parameter and the measured blood flow parameter are each velocity ([0035]-[0036], [0040], [0055]-[0057]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Liu, Dalvin, and Cadieu to analyze blood flow velocities in Doppler images as taught by Sethuraman, as measurement of blood flow velocity provides additional diagnostic information to improve the diagnosis as described by Sethuraman ([0002]-[0003]), and as Doppler and blood flow velocity information may be utilized in analyzing image quality to further aid the user in acquiring high quality images as described by Sethuraman ([0016]).
Response to Arguments
Applicant’s arguments with respect to the claim(s) 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.
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.
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/JONATHAN CWERN/Primary Examiner, Art Unit 3797