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 without traverse of claims 1-5, 17-25, 37-42, 44-46 in the reply filed on 11/12/2025 is acknowledged.
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) 1-5, 17-25, 37-42, 44-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huddleston et al. (US2021/0007603) in view of Wallack et al. (US2021/0202092).
To claim 1, Huddleston teach a method of diagnosing a neurodegenerative disorder (ND) in a patient, the method comprising:
(a) obtaining one or more magnetic resonance images (MRI) of the patient's brain (110 of Fig. 1, 210 of Fig. 2, 310 of Fig. 3, paragraph 0087),
(b) using the one or more MRI images of the patient's brain to segment one or more subcortical structures associated with the ND into subregions, based on structural connectivity to cortical subregions (320 of Fig. 3, paragraphs 0104, 0108),
(c) extracting one or more MRI features from each of the subregions generated by the segmentation in part (b) of the patient's brain (220 of Fig. 2, 330 of Fig. 3, paragraph 0108), and
(d) using one or more machine learning techniques to classify the patient (paragraphs 0068, 0072, 0092-0095, 0098-0100, 0110-0114) as being ND positive or ND negative based on comparisons of the one or more MRI features to at least one training data set, the at least one training data set including MRI features of each of the subregions, thereby diagnosing ND in the patient (Figs. 6-8, paragraphs 0121-0131, true positive or false positive, diagnostic accuracy of the classifier enhances diagnostic confidence (as compared to a clinical assessment alone, classifier can confirm and quantify neurodegenerative changes in neuromelanin and iron on MRI enhances diagnostic confidence).
But, Huddleston do not expressly disclose generated by the segmentation of known ND positive controls and generated by the segmentation of ND negative controls.
Wallack teach training images for MRI diagnostic classification (paragraphs 0012, 0042, 0070) includes positive control training images and negative control training images (paragraphs 0030, 0078).
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 teaching of Wallack into the method of Huddleston, in order to establish validity and reliability of classification.
To claim 20, Huddleston and Wallack teach a method of tracking rate of progression of a neurodegenerative disorder (ND) in a patient and/or prognosticating the symptoms and severity of the ND in the patient (as explained in response to claim 1 above; Huddleston, paragraphs 0033, 0075, 0096, 0100, track progession).
To claim 40, Huddleston and Wallack teach a system to diagnose a neurodegenerative disorder (ND) in a subject (as explained in response to claim 1 above; Huddleston, paragraph 0076-0077, database/dataset, acquisitions and feature extractions to quantify neurodegeneration in multiple different diseases and at different stages of disease, or longitudinally over time. In some embodiments, the one or more classifiers may be trained using datasets from the diseases and stages of interest, which can be cross-sectional or longitudinal).
To claim 2, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the one or more MRI features include measures of surface area, surface displacement relative to average shape of age- matched healthy controls group, volume, connectivity/related white matter tracts, and quantitative MRI parameters (Huddleston, paragraphs 0029, 0123, 0129).
To claim 3, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the MRI includes at least one of T1 weighted structural images, Diffusion-weighted imaging images, magnetization transfer -weighted images, susceptibility-weighted images, T2-weighted images, and quantitative Susceptibility Mapping images, and functional MRI (Huddleston, paragraph 0039).
To claim 4, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the training data set further includes data of ND mimics (Wallack, paragraph 0030, obviously ND related in Huddleston).
To claim 5, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the training data set further includes data of different stages and subtypes of the ND, and wherein the method further comprises classifying the ND stage and subtype of the patient (Huddleston, paragraphs 0005-0007, 0029, stages; paragraph 0037, sub-types).
To claim 17, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the method is cloud-based or computer-based (Huddleston, paragraphs 0077-0078).
To claim 18, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the cortical subreqions are defined using a public MRI atlas (Huddleston, paragraphs 0104, 0107, 0109, segmenting one or more sets of image data into one or more regions of interest using a respective mask and/or atlas, which would have been obviously considered public or standardized due to application for general subjects).
To claim 19, Huddleston and Wallack teach claim 1.
Huddleston and Wallack teach wherein the one or more MRI features are compared (a) to one or more models developed using the at least one training data set and/or (b) to the at least one training data set (Huddleston, paragraphs 0047, 0125, 0130-0131).
To claim 21, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the training data includes prior MRI features of the ND patient (Huddleston, paragraph 0044).
To claim 22, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the one or more MRI features include measures of surface area, surface displacement relative to average shape of age-matched HC group, volume, connectivity, and quantitative MRI parameters (as explained in claim 2 above).
To claim 23, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the MRI includes at least one of T1 weighted structural (T1w) images, Diffusion-weighted imaging (DWI) images, magnetization transfer-weighted images, susceptibility-weighted images, T2-weighted images, quantitative Susceptibility Mapping (QSM) images, Neuromelanin-sensitive MRI images and fMRI images (as explained in response to claim 3 above).
To claim 24, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the training data set further includes data of ND mimics (as explained in response to claim 4 above).
To claim 25, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the training data set further includes data of different stages and subtypes of the ND, and wherein the method further comprises classifying the ND stage and subtype of the patient (as explained in response claim 5 above).
To claim 37, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the method is cloud-based cloud based or computer-based computer based (as explained in response to claim 17 above).
To claim 38, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the cortical subregions are defined using a public MRI atlas (as explained in response to claim 18 above).
To claim 39, Huddleston and Wallack teach claim 20.
Huddleston and Wallack teach wherein the one or more MRI features are compared (a) to one or more models developed using the at least one training data set and/or (b) to the at least one training data set (as explained in response to claim 19 above).
To claim 41, Huddleston and Wallack teach claim 40.
Huddleston and Wallack teach wherein the database further includes MRI features of each of the subregions generated by the segmentation of ND patients whose stage and symptoms of disease are known, and wherein the operations further include estimating stage of the progression of the ND and prognosticating symptoms and severity of the ND that will develop in the patient (Huddleston, paragraphs 0032-0033, 0075, 0096, 0099, monitoring disease progression, prediction/prognostication; paragraphs 0029, 0076, classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a subject), and/or more stages associated with the neurodegenerative disorder(s) and/or the movement disorder(s), using at least quantitative features, such as quantitative MRI feature(s) or measure(s), associated with one or more regions of interest determined from one or more sets of image data of the subject's brain).
To claim 42, Huddleston and Wallack teach claim 40.
Huddleston and Wallack teach wherein the parts (a) to (d) are stored in the cloud and the system is a cloud-based system (as explained in response to claim 17 above).
To claim 44, Huddleston and Wallack teach claim 40.
Huddleston and Wallack teach wherein the system further comprises one or more MRI atlases stored in the cloud, and wherein the cortical subregions are defined said one or more public atlases (as explained in response to claim 18 above).
To claim 45, Huddleston and Wallack teach claim 40.
Huddleston and Wallack teach wherein the system further comprises one or more MRI atlases, and wherein the cortical subregions are defined using said one or more MRI atlases (as explained in response to claim 18 above).
To claim 46, Huddleston and Wallack teach claim 40.
Huddleston and Wallack teach wherein the one or more machine learning techniques are trained (a) with one or more models developed using the database, or (b) with the one or more models developed using the database and with the database (as explained in response to claim 19 above).
Conclusion
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 November 26, 2025