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 .
DETAILED ACTION
Response to Amendment
This is in response to Applicant’s Arguments/Remarks filed on 02/27/2026, which has been entered and made of record.
Response to Arguments
Claim Objection(s)
The objection to the title of the invention as being non-descriptive is withdrawn, as necessitated by amendment.
Rejections - 35 USC § 101
The rejection of claim(s) 1 – 20 under 35 USC § 101 (Judicial Exception/ Non-Statutory Subject Matter) is withdrawn, as necessitated by amendment.
Claim Rejections - 35 USC § 102
Applicant’s arguments regarding the current claim(s) have been fully considered. But, the arguments/remarks are directed to the claims as amended, and so are believed to be answered by and therefore moot in view of the new grounds of rejection presented below.
Status of Claims
Claims 1 – 20 are pending. Claims 1 – 20 are considered below.
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 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Itu, Lucian Mihai (US-20180247020-A1, hereinafter simply referred to as Mihai).
Regarding independent claims 1, 9 and 17, Mihai teaches:
A computer-implemented method of diagnosing, monitoring, screening for or evaluating a musculoskeletal disease or condition of a subject (See at least Mihai, ¶ [0065, 0068]; FIGS. 1 – 3, 17; "…FIG. 17 depicts a confusion matrix and corresponding diagnostics statistics gathered using the techniques described herein…", "…The techniques described herein refer to the usage of machine learning (ML) algorithms for predicting measures of interest related to fracture risk (score, percentage), as extracted typically from finite element analyses: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness; effect of a drug-treatment, e.g. increase in bone strength in time; disease evolution, e.g. decrease in bone strength in time…the online prediction phase is extremely fast, it outputs results in near real-time, and can be run directly on a workstation on-site…"), the method comprising: Quantifying one or more attributes (e.g., cortical thickness of Mihai) of one or more features (e.g., vBMD (volumetric BMD) of Mihai) segmented and identified from a medical image of the subject (See at least Mihai, ¶ [0011, 0030]; FIGS. 1 – 3, 17; "…Bone is segmented from the data and used to create a finite element model (FEM). By applying an FEM solver, a stress analysis may be performed to determine measures of interest related to the strength of the bone. Several measures of interest may be derived from such an analysis including whole-bone strength, load-to-strength ratio, the type of fracture, average stress, average strain, and stiffness…", "…BCT was employed to further evaluate these two strategies and to additionally also compute vBMD (volumetric BMD) from quantitative CT. It was shown that in women with osteoporosis the addition and the switch to teriparatide lead to similar outcomes for the s…"); assessing the quantified attributes of the one or more features with a trained machine learning model comprising a deep learning neural network (e.g., deep learning application of Mihai) trained to diagnose, monitor, screen for or evaluate one or more musculoskeletal diseases or conditions (See at least Mihai, ¶ [0068]; FIGS. 1 – 3, 17; "…The techniques described herein refer to the usage of machine learning (ML) algorithms for predicting measures of interest, as extracted typically from finite element analyses: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness; effect of a drug-treatment, e.g. increase in bone strength in time; disease evolution, e.g. decrease in bone strength in time…the online prediction phase is extremely fast, it outputs results in near real-time, and can be run directly on a workstation on-site…"); and outputting one or more results of the assessing (See at least Mihai, ¶ [0068]; FIGS. 1 – 3, 17; "…The techniques described herein refer to the usage of machine learning (ML) algorithms for predicting measures of interest related to fracture risk (score, percentage), as extracted typically from finite element analyses: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness; effect of a drug-treatment, e.g. increase in bone strength in time; disease evolution, e.g. decrease in bone strength in time…the online prediction phase is extremely fast, it outputs results in near real-time, and can be run directly on a workstation on-site…"), the results comprising fracture risk scores (See at least Mihai, ¶ [0068]; FIGS. 1 – 3, 17; "…The techniques described herein refer to the usage of machine learning (ML) algorithms for predicting measures of interest related to fracture risk (score, percentage), as extracted typically from finite element analyses: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness; effect of a drug-treatment, e.g. increase in bone strength in time; disease evolution, e.g. decrease in bone strength in time…the online prediction phase is extremely fast, it outputs results in near real-time, and can be run directly on a workstation on-site…"); wherein the attributes are one or more of volumetric bone mineral density, cortical porosity, transitional region volumetric bone mineral density, trabecular region volumetric bone mineral density, matrix mineralization level, marrow adiposity, cortical thickness, and trabecular tissue separation (See at least Mihai, ¶ [0083]; FIGS. 1 – 4, 17; "…The features that are used to describe the anatomical model may be morphometric features which are based on various measurements of the bone. As illustrated in FIG. 4, the femur bone morphometric features for the femur bone include, without limitation, cortex thickness…These features may be extracted from 3D data or from one or multiple 2D views, depending on the available medical imaging data…").
Regarding dependent claim(s) 2, 10 and 18, Mihai teaches:
wherein the medical image is an image of a three-dimensional volume of the subject (See at least Mihai, ¶ [0051]; FIGS. 1 – 4, 17; "…FIG. 6 shows 3D anatomical model of a cancellous bone segment…").
Regarding dependent claim(s) 3, 11 and 19, Mihai teaches:
wherein the trained machine learning model is a model trained with training data that comprises subject image data (See at least Mihai, ¶ [0074]; FIGS. 1 – 4, 17; "…Returning to FIG. 1, at step 125, one or more trained ML algorithms are used to predict/estimate one or more measures of interest including, without limitation, existence/severity of osteoporosis/osteopenia; fracture risk (score, percentile, etc.); biomechanical characteristic of interest, as extracted typically from FEA: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness…").
Regarding dependent claim(s) 4 and 12, Mihai teaches:
capturing the medical image of the subject with a scanner (See at least Mihai, ¶ [0105]; FIGS. 1 – 4, 17; "…In case of using the same scanner during examination and visualization, features can be table position, angulations, etc…").
Regarding dependent claim(s) 5, 7, 8, 13, 15, 16 and 20, Mihai teaches:
wherein the results comprise (i) one or more disease classifications; and/or (ii) one or more disease probabilities (See at least Mihai, ¶ [0074]; FIGS. 1 – 4, 17; "…Returning to FIG. 1, at step 125, one or more trained ML algorithms are used to predict/estimate one or more measures of interest including, without limitation, existence/severity of osteoporosis/osteopenia; fracture risk (score, percentile, etc.); biomechanical characteristic of interest, as extracted typically from FEA: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness…any ML algorithm known in the art may be applied including, for example, algorithms based on artificial neural networks (ANN), deep learning, or learning classifier/regression systems…").
Regarding dependent claim(s) 6 and 14, Mihai teaches:
wherein the trained machine learning model (a) is a disease classification model; and/or (b) is a model trained using features extracted from patient data and labels or annotations indicating disease or non-disease (See at least Mihai, ¶ [0074]; FIGS. 1 – 4, 17; "…Returning to FIG. 1, at step 125, one or more trained ML algorithms are used to predict/estimate one or more measures of interest including, without limitation, existence/severity of osteoporosis/osteopenia; fracture risk (score, percentile, etc.); biomechanical characteristic of interest, as extracted typically from FEA: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness…any ML algorithm known in the art may be applied including, for example, algorithms based on artificial neural networks (ANN), deep learning, or learning classifier/regression systems…").
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: See the Notice of References Cited (PTO–892)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IDOWU O. OSIFADE whose telephone number is (571)272-0864. The Examiner can normally be reached on Monday-Friday 8:00am-5:00pm EST.
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/IDOWU O OSIFADE/
Primary Examiner, Art Unit 2675