DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Claim Rejections - 35 USC § 102 2
Claim Rejections - 35 USC § 103 4
Conclusion 17
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 .
Election/Restrictions
Applicant's election with traverse of Group II, claims 1-20, 22-24 in the reply filed on 3/27/26 is acknowledged. The traversal is on the ground(s) that amended claims 21 and 25 shall be included with Group I. The argument is found persuasive and thus all claims are now being treated under the merits.
Claims 1-26 are currently pending.
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 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kang et al (MIA: “Dual-stream Pyramid Registration Network”). Regarding claim 1, Kang discloses a computer implemented method for performing deformable image registration on first volumetric medical image and a second volumetric medical image, the method comprising: (i) for each of the first volumetric medical image and the second volumetric medical image, extracting features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales (see section 3.2); (ii) initiating a mapping between the first volumetric medical image and the second volumetric medical image at a lowest of the plurality of different scales (see section 3.3); (iii) sequentially, for each scale of the plurality of different scales that is above the lowest scale (see section 3.3): predicting a mapping between the first volumetric medical image and the second volumetric medical image at a given scale using a deformation field and features extracted from the first volumetric medical image and the second volumetric medical image at a scale immediately below the given scale (see section 3.3); and correcting the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using features extracted from the first volumetric medical image and the second volumetric medical image at the given scale (see section 3.3, 3.4); the method further comprising: (iv) predicting the deformation field between the first volumetric medical image and the second volumetric medical image at full resolution using the corrected prediction of the mapping at a highest of the plurality of different scales (see section 3.5).
Regarding claim 2, Kang discloses at least one of: a deformation field between the first volumetric medical image and the second volumetric medical image; or a velocity field between the first volumetric medical image and the second volumetric medical image (see abstract, section 3.5).
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 claimedinvention is not identically disclosed as set forth in section 102 of this title, 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 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Mok et al (CV: “Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks”).
Regarding claim 13, Kang teaches all elements as mentioned above in claim 1. Kang does not teach expressly a velocity field between the first volumetric medical image and the second volumetric medical image, and wherein the method further comprises, for each scale of the plurality of different scales: integrating the corrected prediction of the velocity field between the first volumetric medical image and the second volumetric medical image to generate a corrected prediction of a deformation field at the given scale.
Mok, in the same field of endeavor, teaches a velocity field between the first volumetric medical image and the second volumetric medical image, and wherein the method further comprises, for each scale of the plurality of different scales: integrating the corrected prediction of the velocity field between the first volumetric medical image and the second volumetric medical image to generate a corrected prediction of a deformation field at the given scale (see section 2.3).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Mok. The suggestion/motivation for doing so would have been to outperform the existing methods (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Mok continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Chen et al (MIA: “TransMorph: Transformer for unsupervised medical image registration”).
Regarding claims 19-20, Kang teaches all elements as mentioned above in claim 1. Kang does not teach expressly extracting features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales comprises, for an image: partitioning the image into a plurality of volumetric patches; and generating a hierarchical feature map using a self-attention based ML model;
using a transformer ML model to extract features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales.
Chen, in the same field of endeavor, teaches extracting features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales comprises, for an image: partitioning the image into a plurality of volumetric patches; and generating a hierarchical feature map using a self-attention based ML model (see section 3.2);
using a transformer ML model to extract features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales (see section 3.2).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Chen. The suggestion/motivation for doing so would have been to enable substantial performance improvement (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Chen continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 21, 25 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Hardemark (US 2017/0225014 A1).
Regarding claim 21, Kang teaches a method comprising: wherein performing the deformable image registration comprises:(i) for each of the first volumetric medical image and the second volumetric medical image, extracting one or more features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales (see 3.2); (ii) initiating a mapping between the first volumetric medical image and the second volumetric medical image at a lowest of the plurality of different scales (see 3.3): (iii) sequentially, for each scale of the plurality of different scales that is above the lowest scale: predicting a mapping between the first volumetric medical image and the second volumetric medical image at a given scale using a deformation field and at least one of the one or more features extracted from the first volumetric medical image and at least one feature of the one or more features extracted from the second volumetric medical image at a scale immediately below the given scale (see 3.3); and correcting the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using one or more features extracted from the first volumetric medical image and one or more features extracted from the second volumetric medical image at the given scale (see 3.4); (iv) predicting the deformation field between the first volumetric medical image and the second volumetric medical image at full resolution using the corrected prediction of the mapping at a highest of the plurality of different scales (see 3.5). Kang does not teach a computer implemented method for adaptation of a reference Radiotherapy (RT) treatment plan, wherein the reference RT treatment plan is associated with a first volumetric medical image of a patient, the method comprising: acquiring a second volumetric medial image of a patient; performing deformable image registration of the first volumetric medical image and the second volumetric medical image; using the predicted deformation field between the first volumetric medical image and the second volumetric medical image at full resolution to adapt the reference treatment plan.
Hardemark, in the same field of endeavor, teaches a computer implemented method for adaptation of a reference Radiotherapy (RT) treatment plan (see 0003), wherein the reference RT treatment plan is associated with a first volumetric medical image of a patient (see 0025), the method comprising: acquiring a second volumetric medial image of a patient (see 0003, 0025); performing deformable image registration of the first volumetric medical image and the second volumetric medical image (see 0006); using the predicted deformation field between the first volumetric medical image and the second volumetric medical image at full resolution to adapt the reference treatment plan (see 0030, 0036).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Hardemark. The suggestion/motivation for doing so would have been to consider variations in tissue density during treatment (see 0009). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Hardemark continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 25, Kang teaches a method comprising: wherein performing the deformable image registration comprises:(i) for each of the first volumetric medical image and the second volumetric medical image, extracting one or more features from the first volumetric medical image and the second volumetric medical image at each of a plurality of different scales (see 3.2); (ii) initiating a mapping between the first volumetric medical image and the second volumetric medical image at a lowest of the plurality of different scales (see 3.3): (iii) sequentially, for each scale of the plurality of different scales that is above the lowest scale: predicting a mapping between the first volumetric medical image and the second volumetric medical image at a given scale using a deformation field and at least one of the one or more features extracted from the first volumetric medical image and at least one feature of the one or more features extracted from the second volumetric medical image at a scale immediately below the given scale (see 3.3); and correcting the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using one or more features extracted from the first volumetric medical image and one or more features extracted from the second volumetric medical image at the given scale (see 3.4); (iv) predicting the deformation field between the first volumetric medical image and the second volumetric medical image at full resolution using the corrected prediction of the mapping at a highest of the plurality of different scales (see 3.5). Kang does not teach a planning node for adapting a reference Radiotherapy (RT) treatment plan, wherein the reference RT treatment plan is associated with a first volumetric medical image of a patient, and wherein the planning node comprises comprising processing circuitry configured to cause the planning node to: acquire a second volumetric medial image of a patient; perform deformable image registration of the first and second volumetric medical images; use the predicted deformation field between the first volumetric medical image and the second volumetric medical image at full resolution to adapt the reference treatment plan.
Hardemark, in the same field of endeavor, teaches a planning node for adapting a reference Radiotherapy (RT) treatment plan (see 0026), wherein the reference RT treatment plan is associated with a first volumetric medical image of a patient (see 0025), and wherein the planning node comprises comprising processing circuitry (see 0026) configured to cause the planning node to: acquire a second volumetric medial image of a patient (see 0025); perform deformable image registration of the first and second volumetric medical images (see 0006); use the predicted deformation field between the first volumetric medical image and the second volumetric medical image at full resolution to adapt the reference treatment plan (see 00036, 0030).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Hardemark. The suggestion/motivation for doing so would have been to consider variations in tissue density during treatment (see 0009). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Hardemark continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 22, 23 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Liao et al (US 2017/0337682 A1).
Regarding claim 22, Kang teaches a method comprising: (i) for each of a first volumetric medical image and a second volumetric medical image, extract features from the images at each of a plurality of different scales (see 3.2); (ii) initiate a mapping between the first volumetric medical image and the second volumetric medical image at a lowest of the plurality of different scales (see 3.3); (iii) sequentially, for each scale of the plurality of different scales that is above the lowest scale (see 3.3): predict a mapping between the first volumetric medical image and the second volumetric medical image at a given scale using a deformation field and features extracted from the first volumetric medical image and the second volumetric medical image at a scale immediately below the given scale (see 3.3); and correct the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using one or more features extracted from the first volumetric medical image and second volumetric medical image at the given scale (see 3.3, 3.4); (iv) predict the deformation field between the first volumetric medical image and the second volumetric medical image at full resolution using the corrected prediction of the mapping at a highest of the plurality of different scales (see 3.5). Kang does not teach expressly computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, when performed by a computer or a processor, the computer or the processor is caused to.
Liao, in the same field of endeavor, teaches computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, when performed by a computer or a processor, the computer or the processor is caused to (see 0039).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Liao. The suggestion/motivation for doing so would have been to achieve better registration results (see 0005). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Liao continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 23, Kang teaches a method comprising: (i) for each of a first volumetric medical image and a second volumetric medical image, extract features from the images at each of a plurality of different scales (see 3.2); (ii) initiate a mapping between the first volumetric medical image and the second volumetric medical image at a lowest of the plurality of different scales (see 3.3); (iii) sequentially, for each scale of the plurality of different scales that is above the lowest scale (see 3.3): predict a mapping between the first volumetric medical image and the second volumetric medical image at a given scale using a deformation field and features extracted from the first volumetric medical image and the second volumetric medical image at a scale immediately below the given scale (see 3.3); and correct the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using one or more features extracted from the first volumetric medical image and second volumetric medical image at the given scale (see 3.3, 3.4); (iv) predict the deformation field between the first volumetric medical image and the second volumetric medical image at full resolution using the corrected prediction of the mapping at a highest of the plurality of different scales (see 3.5). Kang does not teach expressly a registration node for performing deformable image registration on first volumetric medical image and a second volumetric medical image, the registration node comprising processing circuitry configured to cause the registration node to.
Liao, in the same field of endeavor, teaches a registration node for performing deformable image registration on first volumetric medical image and a second volumetric medical image, the registration node comprising processing circuitry configured to cause the registration node to (see 0039).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang to utilize the cited limitations as suggested by Liao. The suggestion/motivation for doing so would have been to achieve better registration results (see 0005). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang, while the teaching of Liao continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Liao et al (US 2017/0337682 A1), and further in view of Laaksonen et al (US 2020/0104695 A1).
Regarding claim 24, Kang with Liao teaches all elements as mentioned above in claim 23. Kang with Liao does not teach a radiotherapy treatment apparatus.
Laaksonen, in the same field of endeavor, teaches a radiotherapy treatment apparatus (see 0018, 0113, 0004).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang with Liao to utilize the cited limitations as suggested by Laaksonen. The suggestion/motivation for doing so would have been to speed up the workflow (see 0026). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang with Liao, while the teaching of Laaksonen continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al (MIA: “Dual-stream Pyramid Registration Network”) in view of Hardemark (US 2017/0225014 A1), and further in view of Laaksonen et al (US 2020/0104695 A1).
Regarding claim 26, Kang with Hardemark teaches all elements as mentioned above in claim 23. Kang with Hardemark does not teach a radiotherapy treatment apparatus.
Laaksonen, in the same field of endeavor, teaches a radiotherapy treatment apparatus (see 0018, 0113, 0004).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kang with Hardemark to utilize the cited limitations as suggested by Laaksonen. The suggestion/motivation for doing so would have been to speed up the workflow (see 0026). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kang with Hardemark, while the teaching of Laaksonen continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Allowable Subject Matter
Claims 3-12, 14, 15-18 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.
Regarding claims 3-12, none of the references of record alone or in combination suggest or fairly teach wherein predicting the mapping between the first volumetric medical image and the second volumetric medical image at a given scale using the deformation field and features extracted from the first volumetric medical image and the second volumetric medical image at the scale immediately below the given scale comprises: performing trilinear upsampling of the mapping at the scale immediately below the given scale; generating a predicted residual vector field for the given scale using a prediction machine learning (ML) model; and generating a predicted mapping for the given scale by combining a result of the trilinear upsampling with the generated predicted residual vector field.
Regarding claim 14, none of the references of record alone or in combination suggest or fairly teach wherein predicting the mapping between the first volumetric medical image and the second volumetric medical image at a given scale using the deformation field and features extracted from the first and second volumetric medical images at the scale immediately below the given scale comprises: performing trilinear upsampling of the mapping at the scale immediately below the given scale; generating a predicted residual vector field for the given scale using a prediction machine learning, ML, model; and generating a predicted mapping for the given scale by combining a result of the trilinear upsampling with the generated predicted residual vector field; wherein correcting the predicted mapping between the first volumetric medical image and the second volumetric medical image at the given scale using features extracted from the first volumetric medical image and the second volumetric medical image at the given scale comprises: generating a corrected residual vector field using a correction ML model; and generating a corrected mapping for the given scale by combining the result of the trilinear upsampling with a function of the predicted residual vector field and the corrected residual vector field; wherein generating the corrected residual vector field using the correction ML model comprises: generating an input to the correction ML model by using the predicted mapping at the given scale to warp the features extracted from the second volumetric medical image at the given scale, and concatenating the warped features with the features extracted from the first volumetric medical image at the given scale; and causing the correction ML model to process the generated input and to generate an output comprising the corrected residual vector field; wherein using the predicted mapping at the given scale to warp the features extracted from the second volumetric medical image at the given scale comprises: integrating the predicted velocity field at the given scale to obtain a predicted deformation field at the given scale, and using the predicted deformation field at the given scale to warp the features extracted from the second image at the given scale.
Regarding claim 15-18, none of the references of record alone or in combination suggest or fairly teach during a training period: repeating steps (i)-(iv) for a plurality of pairs of first and second volumetric medical images; and updating parameters of the prediction and correction steps to minimize a loss function, wherein the loss function comprises a distillation loss component, and wherein the distillation loss component comprises a loss between the predicted deformation field between the first and second volumetric medical images of a pair at full resolution, and the predicted deformation field between the first and second volumetric medical images of the pair at a given scale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD PARK. The examiner’s contact information is as follows:
Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov
For email communications, please notate MPEP 502.03, which outlines procedures pertaining to communications via the internet and authorization. A sample authorization form is cited within MPEP 502.03, section II.
The examiner can normally be reached on M-F 9-6 CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer, can be reached on (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD PARK/
Primary Examiner, Art Unit 2666
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