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 Objections
Claims 30-32 objected to because of the following informalities: claims 30-32 are dependent on cancelled claim 29. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 1-4 and 7-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PgPub. No. 2018/0259608 by Golden et al. (hereinafter ‘Golden’).
In regards to claim 1, Golden teaches a method for providing a clinical parameter for a predicted target part in a medical image performed by a control unit, the method comprising: acquiring at least one medical image of a target part of a subject from an imaging device; (See Golden Figure 1, Golden teaches medical images acquired by MRI system.)
acquiring predicted result data predicting at least one region for the target part from the at least one medical image using a predictive model trained to predict the at least one region corresponding to the target part based on the at least one medical image; (See Golden paragraphs [0040]-[0042], Golden teaches using a CNN model for segmenting anatomical parts.)
calculating the clinical parameter for the target part based on the predicted result data; and providing the clinical parameter. (See Golden paragraphs [0044], Golden teaches calculating volume of the anatomical structure.)
In regards to claim 2, Golden teaches wherein the predicted result data includes a plurality of segmented images including a mask region obtained by segmenting each predicted region. (See Golden paragraph [0042]-[0043]).
In regards to claim 3, Golden teaches wherein the calculating of the clinical parameter is calculating a median value for a volume of the target part based on a distribution of the mask region for the plurality of segmented images. (See Golden paragraph [0044]).
In regards to claim 4, Golden teaches wherein the providing of the clinical parameter is providing the median value. (See Golden paragraph [0044]).
In regards to claim 7, Golden teaches further comprising: providing an image representing the distribution of the mask region of the plurality of segmented images. (See Golden paragraphs [0043]-[0044]).
In regards to claim 8, Golden teaches wherein the target part includes a heart, and the at least one region includes a left atrium, a left ventricle, a right atrium, and a right ventricle. (See Golden paragraphs [0044] and [0047]).
In regards to claim 9, Golden teaches wherein the predictive model is configured to use a probability model trained to calculate a predictive distribution of a model parameter used when predicting the at least one region for the target part based on the at least one medical image of each of a plurality of recipients. (See Golden paragraph [0043]-[0044]).
In regards to claim 10, Golden teaches wherein the predictive model is configured so that the predictive distribution of the model parameter calculated by the probability model is reflected in a layer for classifying a class of the at least one region. (See Golden paragraph [0170]-[0171]).
In regards to claim 11, Golden teaches wherein the probability model is based on a Bayesian neural network. (See Golden paragraphs [0107]-[0108]).
In regards to claim 12, Golden teaches wherein the predictive model is composed of u-net, and the predictive distribution of the model parameter is applied to a last layer of the predictive model. (See Golden Figure 6 and paragraphs [0107]-[0108]).
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.
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.
Claim(s) 25-28 and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over US PgPub. No. 2018/0259608 by Golden et al. (hereinafter ‘Golden’) in view of US PgPub. 2021/0334955 by Roth et al. (hereinafter ‘Roth’).
In regards to claim 25, Golden teaches a method for screening a medical image for labeling performed by a control unit, comprising: acquiring a plurality of medical images of a target part of a subject from an imaging device; (See Golden Figure 1, Golden teaches medical images acquired by MRI system.)
acquiring predicted result data representing a result of predicting at least one region from the plurality of medical images using a predictive model trained to predict the at least one region corresponding to the target part based on the plurality of medical images; (See Golden paragraphs [0040]-[0042], Golden teaches using a CNN model for segmenting anatomical parts.)
However, Golden does not expressly teach calculating uncertainty data to the predicted result data; and screening a medical image for labeling from the plurality of medical images based on the uncertainty data.
Roth teaches calculating uncertainty data to the predicted result data; and screening a medical image for labeling from the plurality of medical images based on the uncertainty data. (See Roth Figure 4A and paragraph [0066], Roth teaches determining uncertainty values and using the uncertainty value to select images.)
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify Golden to use the training method of Roth. The determination of obviousness is predicated upon the following findings: One skilled in the art would have been motivated to modify Golden in this manner because/in order to be able to leverage highly uncertain data based on model uncertainty by increasing their frequency in the training data.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golden with Roth to obtain the invention as specified in claim 25.
In regards to claim 26, Golden and Roth teach all the limitations of claim 25. Roth also teaches wherein the uncertainty data includes aleatoric uncertainty data, or includes both the aleatoric uncertainty data and epistemic uncertainty data. (See Roth paragraph [0053]).
In regards to claim 27, Golden and Roth teach all the limitations of claim 26. Roth also teaches wherein the screening of the medical image for labeling is determining a medical image related to the epistemic uncertainty data as the medical image for labeling. (See Roth paragraph [0053]).
In regards to claim 28, Golden and Roth teach all the limitations of claim 25. Golden also teaches, wherein the predictive model is configured to use a predictive distribution of model parameters of a probability model trained to predict the at least one region based on at least one medical image of each of a plurality of subjects. (See Golden paragraph [0170]-[0171]).
In regards to claim 30, Golden and Roth teach all the limitations of claim 29. Golden also teaches wherein the predictive model is configured so that the predictive distribution of the model parameter calculated by the probability model is reflected in a layer for estimating a class of the at least one region. (See Golden paragraph [0170]-[0171]).
In regards to claim 31, Golden and Roth teach all the limitations of claim 29. Golden also teaches wherein the predictive model is composed of u-net, and the predictive distribution of the model parameter is applied to a last layer of the predictive model. (See Golden Figure 6 and paragraphs [0107]-[0108]).
Allowable Subject Matter
Claims 5-6 and 32-33 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claims 5-6, the prior art does not teach or suggest wherein the calculating the clinical parameter further includes calculating an error range that includes a difference between the median value and a maximum value of the volume of the target part and a difference between the median value and a minimum value of the volume of the target part.
In regards to claims 32-33, the prior art does not teach or suggest wherein the calculating of the uncertainty data is estimating a variance value of the predictive distribution for the uncertainty of the predicted result data, the estimated variance value includes a first value for aleatoric uncertainty and a second value for epistemic uncertainty, and the screening of the medical image for labeling is determining the at least one medical image, in which the second value corresponds to a preset first threshold value or greater, as the medical image for labeling.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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/UTPAL D SHAH/Primary Examiner, Art Unit 2668
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