DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Election/Restrictions 2
Claim Rejections - 35 USC § 103 3
Conclusion 10
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 I, claims 1-19, in the reply filed on 3/17/26 is acknowledged. The traversal is on the ground(s) that there is no serious search or examination burden. This is not found persuasive because claim 20 is not expressly recited in any of the dependent claims in regards to the same scope of invention.
The requirement is still deemed proper and is therefore made FINAL.
Claims 1-20 are currently pending. Claim 20 is withdrawn from consideration.
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.
Claims 1, 3-5, 8-9, 11, 13, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Schwier et al (US 2022/0392614 A1) in view of Naimark et al (OJSM: “Effect of Muscle Quality on Operative and Nonoperative Treatment of Rotator Cuff Tears”).
Regarding claim 1, Schwier teaches a method comprising: obtaining an image of skeletal muscles of a patient (see 0014-0017, 0034, 0040-0044); inputting the image into a machine learning system trained to segment the muscles in the image (see 0054, 0063-0064, 0091); identifying a property of at least one of the skeletal muscles based on an identification of the segmented muscles (see 0054, 0055, 0092-0093). Schwier does not teach expressly determining a treatment procedure for the patient based on the identified property.
Naimark, in the same field of endeavor, teaches determining a treatment procedure for the patient based on the identified property (see abstract, methods section, discussion section).
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 Schwier to utilize the cited limitations as suggested by Naimark. The suggestion/motivation for doing so would have been to enable a reliable, prognostic indicator used by clinicians when counseling patients (see conclusion). 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 Schwier, while the teaching of Naimark 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 claims 3-5, 8-9, 11, 13, 17-18, Schwier teaches a magnetic resonance image (see 0004, 0040-0042);
a computed tomography image (see 0040-0041, 0120);
sagittal plane (see 0042);
a muscle quality(see 0083, 0091-0094);
a fat fraction of the muscle (see 0049-0050, 0055, 0083, 0092-0095, 0050);
pre-processing the obtained image prior to inputting the image into the trained machine learning system (see 0048, 0089, 0097, 104);
a shoulder muscle (see 0017, 0042-0044);
a two-dimensional image from a computed tomography or magnetic resonance three-dimensional volume (see 0041, 0113);
inputting a plurality of images into the machine learning system, the plurality of images being of a three-dimensional volume of the skeletal muscles, wherein the machine learning system is trained to segment the muscles in the three-dimensional volume (see 0041, 0044, 0056, 0113, 0054, 0063-0064, 0091).
Regarding claim 16, Schwier with Naimark teaches all elements as mentioned above in claim 1. Schwier with Naimark does not teach expressly determining a likelihood of success of a plurality of different treatment procedures and identifying the treatment procedure having the greatest likelihood of success.
Naimark, in the same field of endeavor, teaches determining a likelihood of success of a plurality of different treatment procedures and identifying the treatment procedure having the greatest likelihood of success (see abstract, method).
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 Schwier with Naimark to utilize the cited limitations as suggested by Naimark. The suggestion/motivation for doing so would have been to enable a reliable, prognostic indicator used by clinicians when counseling patients (see conclusion). 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 Schwier with Naimark, while the teaching of Naimark 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 2, 6-7, 10, 19 is rejected under 35 U.S.C. 103 as being unpatentable over Schwier et al (US 2022/0392614 A1) in view of Naimark et al (OJSM: “Effect of Muscle Quality on Operative and Nonoperative Treatment of Rotator Cuff Tears”) with Riem et al (RAI: “Deep learning algorithm for automatic 3d segmentation of rotator cuff muscle and fat from clinical mri scans”).
Regarding claims 2, 6-7, 10, 19, Schwier with Naimark teaches all elements as mentioned above in claim 1. Schwier with Naimark does not teach expressly generating a visualization of the segmentation muscles as an output of the trained machine learning system, or based on the trained machine learning system;
a three-dimensional muscle volume;
a number of pixels or voxels within the segmented muscle;
a multi-class classifier and is trained to segment at least three muscles of a shoulder of the patient;
inputting a plurality of images into the machine learning system, the plurality of images being of a three-dimensional volume of the skeletal muscles, wherein the machine learning system is trained to output a three-dimensional volume in which the plurality of skeletal muscles are segmented.
Riem, in the same field of endeavor, teaches generating a visualization of the segmentation muscles as an output of the trained machine learning system, or based on the trained machine learning system (see AI framework);
a three-dimensional muscle volume (see summary);
a number of pixels or voxels within the segmented muscle (see statistical analysis/model performance section);
a multi-class classifier and is trained to segment at least three muscles of a shoulder of the patient (see training dataset section);
inputting a plurality of images into the machine learning system, the plurality of images being of a three-dimensional volume of the skeletal muscles, wherein the machine learning system is trained to output a three-dimensional volume in which the plurality of skeletal muscles are segmented (see training dataset/AI model).
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 Schwier with Naimark to utilize the cited limitations as suggested by Riem. The suggestion/motivation for doing so would have enable more variability in images (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 Schwier with Naimark, while the teaching of Riem 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 12 is rejected under 35 U.S.C. 103 as being unpatentable over Schwier et al (US 2022/0392614 A1) in view of Naimark et al (OJSM: “Effect of Muscle Quality on Operative and Nonoperative Treatment of Rotator Cuff Tears”) with Carnell et al (US 2022/0222816 A1).
Regarding claim 12, Schwier with Naimark teaches all elements as mentioned above in claim 11. Schwier with Naimark does not teach expressly performing a contrast limited adaptive histogram equalization (CLAHE).
Carnell, in the same field of endeavor, teaches performing a contrast limited adaptive histogram equalization (CLAHE) (see 0054).
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 Schwier with Naimark to utilize the cited limitations as suggested by Carnell. The suggestion/motivation for doing so would have identify rapidly lesions (see 0007). 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 Schwier with Naimark, while the teaching of Carnell 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 14 is rejected under 35 U.S.C. 103 as being unpatentable over Schwier et al (US 2022/0392614 A1) in view of Naimark et al (OJSM: “Effect of Muscle Quality on Operative and Nonoperative Treatment of Rotator Cuff Tears”) with Agosti et al (MRMPBM: “Deep learning for automatic segmentation of thigh and leg muscles”).
Regarding claim 14, Schwier with Naimark teaches all elements as mentioned above in claim 11. Schwier with Naimark does not teach expressly a knee or thigh muscle.
Agosti, in the same field of endeavor, teaches a knee or thigh muscle (see abstract).
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 Schwier with Naimark to utilize the cited limitations as suggested by Agosti. The suggestion/motivation for doing so would have enable high performance (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 Schwier with Naimark, while the teaching of Agosti 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 15 is rejected under 35 U.S.C. 103 as being unpatentable over Schwier et al (US 2022/0392614 A1) in view of Naimark et al (OJSM: “Effect of Muscle Quality on Operative and Nonoperative Treatment of Rotator Cuff Tears”) with Marzola et al (CBM: “Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment”).
Regarding claim 15, Schwier with Naimark teaches all elements as mentioned above in claim 11. Schwier with Naimark does not teach expressly an elbow muscle.
Marzola, in the same field of endeavor, teaches an elbow muscle (see abstract).
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 Schwier with Naimark to utilize the cited limitations as suggested by Marzola. The suggestion/motivation for doing so would have enable robust performance and help in neuromuscular disease diagnosis (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 Schwier with Naimark, while the teaching of Marzola 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.
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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EDWARD PARK/
Primary Examiner, Art Unit 2666
Prosecution Insights