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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-6, 10-15, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Canovas et al. “Study of carpal bone morphology and position in three dimensions by image analysis from computed tomography scans of the wrist”. Surg Radiol Anat 26, 186–190 (2004). https://doi.org/10.1007/s00276-003-0207-x.
Regarding claim 1, Canovas teaches a method (Abstract), comprising:
receiving a three-dimensional medical image of a body part that includes a plurality of bones (CT scans; page 186 col 2 to page 187 col 1);
identifying each of the bones in the three-dimensional medical image (Each bone was then identified; page 187 col 2);
generating a three-dimensional computer model that includes a three-dimensional representation of each bone identified in the three-dimensional medical image (bone in 3D space; page 187 col 2); and
identifying bone distances between each bone in the body part by measuring the distances between each three-dimensional representation of each bone (distance from the centroid of one carpal bone to that of another; page 187 col 2).
Regarding claim 2, Canovas teaches the method of claim 1, wherein the three-dimensional medical image is a computed tomography (CT) scan or a magnetic resonance image (MM) (CT scans; page 186 col 2 to page 187 col 1).
Regarding claim 3, Canovas teaches the method of claim 1, wherein the bone distances are the shortest distances between each three-dimensional representation of each bone or the centroid distances between the centroids of each three-dimensional representation of each bone (distance from the centroid of one carpal bone to that of another; page 187 col 2 and page 189 col 1 Table 2).
Regarding claim 4, Canovas teaches the method of claim 1, further comprising: comparing the bone distances to reference data (measurements compared with normal wrists; page 190 col 1).
Regarding claim 5, Canovas teaches the method of claim 4, wherein the reference data includes the bone distances identified using a previous medical image of the body part (measurements of normal wrists; page 190 col 1).
Regarding claim 6, Canovas teaches the method of claim 4, wherein the reference data includes thresholds generated by analyzing the bone distances of patients diagnosed with conditions affecting joint spacing (mean distances; Table 2 and page 190 col 1).
Regarding claim 10, Canovas teaches the method of claim 1, wherein at least two of the plurality of bones overlap when viewed along an axis that is orthogonal to the shortest vector between any two of the plurality of bones (Figs. 1-6).
Regarding claim 11, Canovas teaches a joint space quantification system (page 187 col 1), comprising:
non-transitory computer readable storage media that stores a three-dimensional medical image of a body part that includes a plurality of bones (digital data; page 187 col 1); and
a hardware computer processor that (graphic workstation; page 187, col 1):
identifies each of the bones in the three-dimensional medical image (Each bone was then identified; page 187 col 2);
generates a three-dimensional computer model that includes a three-dimensional representation of each bone identified in the three-dimensional medical image (bone in 3D space; page 187 col 2); and
identifies bone distances between each bone in the body part by measuring the distances between each three-dimensional representation of each bone (distance from the centroid of one carpal bone to that of another; page 187 col 2).
Claims 12-15, 19 and 20 recite similar limitations as claims 2-6, 10, and 1 thus, arguments similar to that presented above for claims 2-6, 10, and 1 are equally applicable to claims 12-15, 19 and 20.
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) 7-9 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Canovas as applied to claims 1, 4 and 11 above, and further in view of Panes Saavedra et al. (US 2024/0268699).
Regarding claim 7, Canovas teaches the method of claim 4, but does not explicitly teach wherein comparing the bone distances to the reference data comprises applying a multivariate model generated by a neural network trained using the bone distances and biological data of patients diagnosed with conditions affecting joint spacing.
However, Panes Saavedra teaches wherein comparing the bone distances to the reference data comprises applying a multivariate model generated by a neural network trained using the bone distances and biological data of patients diagnosed with conditions affecting joint spacing (trained convolutional neural network predicts joint-related conditions based on joint space width progression; ¶ 0011, ¶ 0089 and ¶¶ 0103-0105).
Canovas and Panes Saavedra are in the same field of endeavor of a method and system for evaluating joints. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method and system of Canovas to apply a deep learning model as taught by Panes Saavedra. The combination improves the method and system by providing an autonomous process to analyze medical images of a patient to diagnose degenerative joint diseases.
Regarding claim 8, Canovas in view of Panes Saavedra teach the method of claim 7, but Canovas does not explicitly teach wherein the biological data includes age, height, or weight.
However, Panes Saavedra teaches wherein the biological data includes age, height, or weight (patient data; ¶ 0005 and ¶ 0061).
The motivation applied in claim 7 is incorporated herein.
Regarding claim 9, Canovas in view of Panes Saavedra teach the method of claim 7, but Canovas does not explicitly teach wherein the machine learning model is also trained using biomechanics data of the patients diagnosed with conditions affecting joint spacing.
However, Panes Saavedra teaches wherein the machine learning model is also trained using biomechanics data of the patients diagnosed with conditions affecting joint spacing (¶ 0061).
The motivation applied in claim 7 is incorporated herein.
Claims 16-18 recite similar limitations as claims 7-9 thus, arguments similar to that presented above for claims 7-9 are equally applicable to claims 16-18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Long et al. (US 2024/0261030) teaches a system and a method for processing and displaying images to provide clinical decision intelligence and to optimize outcomes after joint replacement procedures.
Katayama et al. (US 2023/0419493) teaches an estimation device that includes a learner that is trained using X-ray images to predict joint destruction.
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/KENT YIP/Primary Examiner, Art Unit 2681