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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 of an Amendment with a Request for Continued Examination filed on March 29, 2026 (herein “Amendment”) has been entered.
Response to Arguments
Applicant’s arguments and amendments to the claims in the Amendment with respect to the rejection of claims 1–25 as being directed to an abstract idea without significantly more or a practical application under 35 U.S.C. 101 have been fully considered and are persuasive. Specifically, Applicant’s remarks set forth on pages 13–15 of the Amendment regarding the amendments to the claims clarifying and reciting the elements that realize the improvement to the technical field, and thus recite a practical application and significantly more are persuasive. The rejection of presently pending claims 1–22 and 24–25 under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments and amendments in the Amendment with respect to the rejections of claims 1 and 6, and claims depending therefrom under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made for claims 1–5 in view of Isaksson et al., “Quality assurance for automatically generated contours with additional deep learning,” Insights into Imaging, 2022, 13:137, https://doi.org/10.1186/s13244-022-01276-7. Specifically, while Sirjani does teach training a machine learning model to generate difference parameters, in the portions of Sirjani previously relied upon to correspond to the claimed “generate difference parameters,” Sirjani teaches generating difference parameters during training, but not “after training” as newly recited in the new amendment: “wherein after training, the difference model uses the input image or the input video to generate difference parameters without requiring the software-tracked contour or the adjusted contour.” However, at least for these limitations, newly cited Isaksson is relied upon.
Claim Objections
Claims 12, 18 (and therefore claims 19–22 and 24 which depend therefrom), and 25 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim is recited in claims 12, 18 and 25 not in the alternative. See MPEP § 608.01(n). For claim 12, it is recited as depending from claims 6 and 1, for claim 18, it is recited as depending from claims 1 and 6, and for claim 25, it is recited as being dependent from claims 1 and 6. Accordingly, claims 12, 18–22, 24 and 25 have not been further treated on the merits, except as noted below that if the dependency from claim 6 is maintained, while amending to overcome the present objection, claims 12, 18–22, and 24–25 will be allowable over the cited art as set forth in detail below.
Claim Rejections - 35 USC § 112
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 and therefore, claims 2–5 which depend therefrom, are 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. Specifically, claim 1 recites “to generate difference parameters” in line 2, and then again “to generate difference parameters” in lines 17–18. Therefore, it is unclear and indefinite whether the “difference parameters” in lines 17–18 are intended to be the same as those recited in line 2, or if they are different difference parameters altogether.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1–5 are rejected under 35 U.S.C. 103 as being unpatentable over Sirjani et al., “Automatic cardiac evaluations using a deep video object segmentation network,” Insights Imaging 13, 69 (April 8, 2022), https://doi.org/10.1186/s13244-022-01212-9 (herein “Sirjani”) in view of Isaksson et al., “Quality assurance for automatically generated contours with additional deep learning,” Insights into Imaging, 2022, 13:137, https://doi.org/10.1186/s13244-022-01276-7 (herein “Isaksson”).
Regarding claim 1, with deficiencies noted in square brackets [], Sirjani teaches a method of training a difference model (Sirjani pages 3–4, figs. 1 and 2, training of the EchoRCNN (model)) to generate difference parameters related to the differences between a software-tracked contour of an object and an adjusted contour of the object in an input image or an input video (Sirjani pages 3-4, the EchoRCNN including a regression network which predicts (generates) from input frames of a cardiac cycle video, offsets (difference) between A anchors from spatial positions of an object from a classification subnet (software-tracked contour), and a ground truth from a manually refined border (contour)), comprising:
training a first machine learning model with multiple first training data sets, each of the multiple first training data sets comprises a first training image set as the input for training, and a training difference parameter set as [the target for training] (Sirjani pages 3–4, fig. 1, training the network using LV and RV datasets, the datasets described on pages 2–3 as being 2D echocardiography frames from a video, and where one of the inputs to the segmentation subnet is the output of the regression subnet, described on page 4 as being a predicted offset (difference) between anchor points A, which are training difference parameters because they are offsets/differences determined during training), wherein the first training image set and the training difference parameter set are derived from a first training video (Sirjani page 2, right column, “Materials and methods” section, training of the EchoRNN using 750 selected echocardiography sequences as video with 45 frames on average, and where each video is processed one by one as shown in fig. 1, and disclosed on page 3, EchoRCNN architecture section) and generated by the steps of:
(a) obtaining the first training image set by selecting at least one image frame from the first training video (Sirjani page 2, collection of 2D echocardiography series were prepared from selecting 750 view series with 45 frames on average, which are selected videos (first training video) with proper LV (left ventricle) shapes, where fig. 1, and page 3, EchoRCNN architecture processing each frame of the video);
(b) generating, by an analysis software, a training software-tracked contour of the object from the first training video or the first training image set (Sirjani page 2, delineation (software-tracked contour) upon frames of each view series (first training video) was performed using the Auto 2D Quantification (a2DQ) tool in the Qlab Cardiac Analysis (analysis software));
(c) obtaining a training adjusted contour of the object (Sirjani page 2, users manually refine the points on the walls to correct the estimated region for LV (the object), the manual refined points used in the course of training the EchoRCNN and thus being a training adjusted contour); and
(d) obtaining the training difference parameter set based on the training software-tracked contour and the training adjusted contour (Sirjani page 4, the four outputs of the regression subnet is an offset (difference parameter) between A anchors from spatial positions of an object from a classification subnet (software-tracked contour), and a ground truth from a manually refined border (adjusted contour));
[wherein after training, the difference model uses the input image or the input video to generate difference parameters without requiring the software-tracked contour or the adjusted contour].
While Sirjani teaches that offsets are predicted by a regression subnet and input into the segmentation subject, Sirjani does not explicitly teach that the offsets are “the target for training.”
Further, Sirjani does not explicitly teach “wherein after training, the difference model uses the input image or the input video to generate difference parameters without requiring the software-tracked contour or the adjusted contour.”
Isaksson teaches a difference parameter set as the target for training (Isaksson page 3, “Predicting contour quality” section, the target for training the contour quality model was two parameters that measure the difference between a machine determined contour and a ground truth manually generated contours, these two parameters being the mean absolute error and the Spearman rank correlation between a predicted Dice coefficient and the target Dice coefficient).
Isaksson further teaches wherein after training, the difference model uses the input image or the input video to generate difference parameters without requiring the software-tracked contour or the adjusted contour (Isaksson page 9, Abstract, the deep learning model is trained so that in practice (after training), the model monitors the performance (by outputting a DICE score – generating difference parameters) using automated contour models, without the training data contours, thus without requiring the software-tracked contour or the adjusted contour).
Therefore, taking the teachings of Sirjani and Isaksson together as a whole, it would have been obvious to a person having ordinary skill in the art (herein “PHOSITA”) before the effective filing date of the claimed invention to have modified the regression subnet of Sirjani to include contour difference values as training targets and generating contour difference values in practice after training without needing the training data as disclosed in Isaksson, at least because doing so would allow for ensuring quality and monitoring the performance of deployed automated contouring models. See Isaksson Conclusions section on page 9.
Regarding claim 2, Sirjani teaches wherein each image in the first training image set is an echocardiographic image, and where the object is endocardium (Sirjani pages 2–4, training using LV and RV datasets from 2D echocardiography series, where LV (left ventricles) and RV (right ventricles) include respective endocardium).
Regarding claim 3, Sirjani teaches wherein each image of the first training image set is processed according to the software-tracked contour before used as the input for training (Sirjani pages 2–3, Fig. 1, delineation (software-tracked contour) was performed using the Auto 2D Quantification (a2DQ) tool as a first step before pre-processing and inputting into the main EchoRNN network).
Regarding claim 4, Sirjani teaches wherein the first machine learning model is a regression model based on convolutional neural network (Sirjani page 4, Figs. 1 and 2, regression subnet with convolutional layers like the classification subnet).
Regarding claim 5, Sirjani teaches wherein the first machine learning model is a residual neural network (ResNet) model (Sirjani Fig. 3, page 5, “box subnet” which is the regression subnet comprising a ResNet50 backbone).
Allowable Subject Matter
Claim 6, and claims 7–17 which depend therefrom, and claims 18–22, 24 and 25 which presently recite a dependency from claim 6 would be allowable if rewritten to overcome any claim objections or claim rejections set forth above under 35 U.S.C. 112(b). The following is a statement of reasons for the indication of allowable subject matter: the closest cited art of record includes Sirjani in combination with Isaksson, as applied above to claim 1. Further, Isaksson, while teaching “the software-generated analysis result is derived from at least one software-tracked contour of the object generated by the analysis software from the at least one input image or the input video; the adjusted analysis result is derived from at least one adjusted contour of the object in the at least one input image or the input video; and after training, the evaluation model uses at least one difference parameter set,” as given in pages 3–4 of Isaksson teaching evaluating the quality for automatically generated contours (software-tracked contour) against ground truth segmentations/contours determined by human experts (adjusted analysis result from at least one adjusted contour), and that after training the trained machine learning model in Isaksson (corresponding to the claimed evaluation model) uses a difference parameter set of a mean absolute error and Spearman rank of Dice coefficient values. Neither Isaksson, nor Sirjani, nor any of the other cited art of record, whether considered alone or in an obvious combination, teach or suggest to a person having ordinary skill in the art, the limitations recited in claim 6 of “after training, the evaluation model uses … and at least one geometric parameter set to generate the predicted evaluation errors, wherein each of the at least one geometric parameter set comprises one or more geometric parameters calculated based on one of the at least one software-tracked contour,” and all other supporting limitations thereof in claim 6.
Therefore, no combination of the cited art of record, whether considered alone, or in a combination obvious to a PHOSITA, teach or suggest the limitations of claim 6, and therefore claims 7–17 which depend therefrom, and claims 18–22, 24 and 25 which presently recite a dependency from claim 6.
Conclusion
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MICHELLE M. KOETH
Primary Examiner
Art Unit 2671
/MICHELLE M KOETH/Primary Examiner, Art Unit 2671
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