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 with respect to claim(s) 1-20 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.
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.
Claim(s) 1-4, 7-10, 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karade et al. (US2021/0007806) in view of Trautwein et al. (US2018/0206812).
To claim 1, Karade teach a method for correcting a 2D measurement value (abstract, Fig. 28), the method comprising:
receiving 2D image data of an examination object in a first 3D orientation (paragraphs 0075-0076);
determining a value of a 2D measurement based on the 2D image data;
detecting landmarks in the 2D image data; estimating 2D positions of the landmarks (Fig. 3A; paragraphs 0085-0089); and
predicting a corrected 2D measurement value of the examination object using a trained model (Figs. 23, 28; paragraphs 0195-0225) based on the 2D image data (paragraphs 0168-0169, 0174, template projection contour points may be adapted to the input contour using self-organizing maps technique, wherein the learning process of said adaptability is considered trained), the 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object (paragraphs 0095, 0109-0121).
But, Karade do not expressly disclose the corrected 2D measurement value being a value of the 2D measurement with the examination object in the reference 3D orientation, and the reference 3D orientation being different from the first 3D orientation.
Trautwein teach using machine learning methods (paragraphs 0033, 0070-0071, with machine learning methods or with trained neural networks) to correct 2D measurement (paragraph 0031, X-ray image is measured using the corrected digitally reconstructed X-ray picture in the output plane) in reference 3D orientation (paragraphs 0010, 0023, 0028, 0037, 0040, 0043, 0062, 0067, 0069, 0074-0075, defining a corrected projection direction and an associated output plane, on the basis of 3D model, wherein positional deviation of the 3D objects relative to one another between the X-ray image and the 3D model may come about due to different orientations of the patient), wherein the reference 3D orientation being different from the first 3D orientation (paragraphs 0062, 0075, orientation deviations, measuring the X-ray image includes determining a correction function that results from the deviation between the corrected virtual projection direction and the projection direction and applying the correction function to the measurement results).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teachings of Trautwein into the method of Karade with machine learning based algorithm, in order to improve adaptability on measurement correction.
To claim 12, Karade and Trautwein teach a correction device, comprising:
an input interface to receive 2D image data of an examination object; a landmark detection unit to detect landmarks in the 2D image data, and to estimate 2D positions of the landmarks; and a prediction unit to predict a corrected measurement value of the examination object using a trained model, the trained model based on the 2D image data, the 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object (as explained in response to claim 1 above).
To claim 13, Karade and Trautwein teach a medical imaging system, comprising:
an acquisition unit to acquire measuring data from an examination object; a post-processor to generate post-processed 2D image data based on the measuring data; and
the correction device according to claim 12 (as explained in response to claim 12 above).
To claim 14, Karade and Trautwein teach a non-transitory computer program product with a computer program, which is loadable into a memory device of a medical imaging system, the computer program including program sections that, when executed by the medical imaging system, cause the medical imaging system to perform the method according to claim 1 (as explained in response to claim 1 above).
To claim 15, Karade and Trautwein teach a non-transitory computer readable medium storing program sections that, when executed by at least one processor of a medical imaging system, cause the medical imaging system to perform the method according to claim 1 (as explained in response to claim 1 above).
To claim 16, Karade and Trautwein teach a correction device, comprising: a memory storing computer-executable instructions; and at least one processor configured to execute the computer-executable instructions to cause the correction device to detect landmarks in 2D image data of an examination object, estimate 2D positions of the landmarks, and predict a corrected measurement value of the examination object using a trained model, the trained model based on the 2D image data, the 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object (as explained in response to claim 1 above).
To claim 2, Karade and Trautwein teach claim 1.
Karade and Trautwein teach wherein the examination object comprises at least one of an organ of a patient, a part of a body of the patient, a limb of the patient, or a chest of the patient (Karade, Fig. 28).
To claim 3 Karade and Trautwein teach claim 1.
Karade and Trautwein teach wherein the trained model comprises a first trained model and a second trained model to be carried out one after another (obvious in Karade, page 521, having different trained models for various optimizers and learning parameters; obvious in Trautwein, paragraphs 0070-0071, trained neural networks in conjunction).
To claim 4, Karade and Trautwein teach claim 3.
Karade and Trautwein teach wherein an input of the first trained model includes the 2D image data, and an output of the first trained model includes estimated 3D orientation parameters (Karade, paragraphs 0152-0153, 0164).
To claim 7, Karade and Trautwein teach claim 3.
Karade and Trautwein teach wherein the first trained model is trained based on a multitude of synthetic 2D images of the examination object corresponding to different 3D orientation parameters (Karade, Fig. 19; paragraphs 0005, 0095, 0125-0128, 0152-0153).
To claim 8, Karade and Trautwein teach claim 4.
Karade and Trautwein teach further comprising: estimating the estimated 3D orientation parameters, the estimating including segmenting anatomical structures of the 2D image data,
localizing the segmented anatomical structures, and predicting 3D orientation parameters based on positions of the localized segmented anatomical structures (Karade, paragraphs 0084, 0096, 0109-0119, 0146-0148).
To claim 9, Karade and Trautwein teach claim 8.
Karade and Trautwein teach wherein the first trained model is configured to carry out the segmenting, which is trained by a multitude of synthetic 2D images, and wherein the output of the first trained model includes a label mask with segmentations (as explained in response to claims 7-8 above).
To claim 10, Karade and Trautwein teach claim 9.
Karade and Trautwein teach wherein measurement values concerning the positions of the localized segmented anatomical structures are taken from the label mask, and the 3D orientation parameters are predicted based on the measurement values (as explained in response to claims 8-9 above).
Claim(s) 5-6, 11, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karade et al. (US2021/0007806) in view of Trautwein et al. (US2018/0206812) and Georgescu et al. (US2016/0174902).
To claim 5, Karade and Trautwein teach claim 4.
Karade and Trautwein teach wherein an input for the second trained model includes the estimated 3D orientation parameters, the reference parameter of the reference 3D orientation of the examination object, and the value of the 2D measurement to be corrected, the value of the 2D measurement value depending on the 2D positions of the landmarks, and an output of the second trained model includes the corrected 2D measurement value (obvious in view of multi-trained model teachings in Karade and Trautwein).
In furthering said obviousness, Georgescu teach an input for the second trained model includes the estimated orientation parameters, the reference parameter of the reference orientation of the examination object, and the value of the measurement to be predicted, the value of the measurement value depending on the positions of the landmarks, and an output of the second trained model includes the predicted measurement value (Figs. 4-6, paragraphs 0036 0059, 0062-0064, 0075, 0080-0082, 0109-0110, object position estimation and steerable features for position-orientation estimation and position-orientation-scale estimation), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Karade and Trautwein, in order to implement object position-orientation-scale estimation with trained cascade models.
To claims 6, 17 and 18, Karade, Trautwein and Georgescu teach claims 3, 4 and 5.
Karade, Trautwein and Georgescu teach wherein an input of the second trained model includes a difference between an estimated 3D orientation and the reference 3D orientation (Georgescu, paragraphs 0059-0063, difference vector).
To claims 11, 19 and 20, Karade, Trautwein and Georgescu teach claims 4, 5 and 8.
Karade, Trautwein and Georgescu teach wherein at least one of estimating of the estimated 3D orientation parameters in the 2D image data includes estimating a probability density function of the estimated 3D orientation parameters in the 2D image data, or the predicting of the corrected measurement value of the examination object includes determining a probability density function of the corrected measurement value (obvious with Georgescu, paragraph 0092).
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 or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 June 17, 2026
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