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 .
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 5-6, 12-13, 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D1.1
With regard to claim 1, D1 teach computer-implemented method for spine segmentation and classification, the method comprising:(a) receiving a medical image of a subject, wherein the medical image captures one or more structures of the subject (see ¶ 52, fig. 6, 8: receiving image); (b) applying a first deep network to the medical image and outputting a detection result, wherein the detection result comprises at least a segmentation map of the one or more structures and a location predicted for the one or more structures (see ¶¶ 52, 60: deep learning; segmentation mask and coordinates of structures); (c) generating an input to a second deep network based at least in part on the location predicted in (b) (see ¶ 52: position data input for subsequent processing; ¶¶ 54, 57, 60: deep neural network); and (d) predicting a degenerative condition for the one or more structures by processing the input using the second deep network (see ¶ 57: classifying degenerative changes).
With regard to claim 2, D1 teach computer-implemented method of claim 1, wherein the medical image includes a magnetic resonance image and the one or more structures comprise one or more spine structures (see ¶¶ 42, 57, 59, 65-66: vertebra).
With regard to claim 5, D1 teach computer-implemented method of claim 3, wherein the segmentation model is trained to predict the segmentation map of the one or more structures (see ¶¶ 52, 57: segmentation mask).
With regard to claim 6, D1 teach computer-implemented method of claim 1, wherein the input to the second deep network comprises one or more patches generated from the medical image based at least in part on the location predicted in (b) (see ¶¶ 52, 54, 57, 60: location of anatomical structure or segmentation mask input for further processing).
With regard to claim 12, see discussion of claim 1.
With regard to claim 13, see discussion of claim 2.
With regard to claim 16, see discussion of claim 5.
With regard to claim 17, see discussion of claim 6.
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 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.
Claims 3-4, 7-11, 14-15, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over D1.
With regard to claim 3, D1 teach computer-implemented method of claim 1, but fails to explicitly teach wherein the first deep network comprises a segmentation model with a dual regulation module. However, Examiner takes Official Notice to the fact that dual regression regularization models are extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate the dual regression model in the deep learning network for enhanced feature discrimination, stability and robustness with low computational overhead.
With regard to claim 4, D1 teach computer-implemented method of claim 3, wherein the see ¶ 52: predict coordinates of structures). Examiner takes Official Notice to the fact that dual regression regularization models are extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate the dual regression model in the deep learning network for enhanced feature discrimination, stability and robustness with low computational overhead.
With regard to claim 7, D1 teach computer-implemented method of claim 1, wherein the input to the second deep network see ¶¶ 52, 54, 57, 60: segmentation mask). D1 teaches generating a segmentation mask, but fails to explicitly teach determining attention map. However, Examiner takes Official Notice to the fact that generating attention maps is extremely well known in the art before the effective filing date and that it would have been obvious to incorporate known teachings in to the configuration of D1 yielding predictable results. The motivation for calculating attention map would have been to differentiate between more important regions from less relevant regions.
With regard to claim 8, D1 fails to explicitly teach computer-implemented method of claim 1, wherein the input to the second deep network comprises at least a second medical image of a view that is different from the first medical image. However, Examiner takes Official Notice to the fact that analyzing images with different views is extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate known teachings into the configuration of D1 yielding predictable and enhanced results because images from different views provide additional information on the structures that are being analyzed.
With regard to claim 9, D11 fails to explicitly teach computer-implemented method of claim 1, wherein the second deep network comprises a plurality of branches. However, Examiner takes Official Notice to the fact that deep learning networks with plurality of branches or channels is extremely well known in the art before the effective filing date and that one skilled in the art would have been motivated to incorporate known teachings into the configuration of D1 yielding predictable and enhanced results by having different branches specialize in extracting different types of features, specialized functionality etc.
With regard to claim 10, D1 fails to explicitly teach computer-implemented method of claim 9, wherein the input comprises patches of at least two different sizes and wherein at least two of the plurality of branches are configured to process the patches of at least two different sizes respectively. However, Examiner takes Official Notice to the fact that multi-scale feature extraction via multiple channels or branches is extremely well known in the art before the effective filing date and would have been particularly obvious to incorporate known teachings into the configuration of D1 yielding predictable and enhanced multi-scale features for better discrimination of features.
With regard to claim 11, D1 fails to explicitly teach computer-implemented method of claim 9, wherein the input comprises patches of at least two different views and wherein at least two of the plurality of branches are configured to process the patches of at least two different views respectively. However, Examiner takes Official Notice to the fact that analyzing images with different views is extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate known teachings into the configuration of D1 yielding predictable and enhanced results because images from different views provide additional information on the structures that are being analyzed.
With regard to claim 14, see discussion of claim 3.
With regard to claim 15, see discussion of claim 4.
With regard to claim 18, see discussion of claim 7.
With regard to claim 19, see discussion of claim 8.
With regard to claim 20, see discussion of claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVINASH YENTRAPATI whose telephone number is (571)270-7982. The examiner can normally be reached on 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 US Publication No. 2021/0174503.