DETAILED ACTION
Notice of 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
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.
Claim(s) 1-4 and 20-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Machine Learning on Human Connectome Data from MRI” by C.J. Brown et al. arXiv, 1611.08699, 2016 (Brown).
Regarding claims 1, 20, and 21, Brown discloses a method of generating a multi-modal normative model of a brain, the method comprising: receiving functional magnetic resonance imaging (fMRI) data and diffusion MRI (dMRI) data for each of a plurality of human subjects; generating, based on the fMRI data, functional connectivity data for each of the plurality of human subjects; generating, based on the dMRI data, structural connectivity data for each of the plurality of human subjects (p.6-8: “2.1 Structural Connectomes” - dMRI data describe structural connections; “2.2 Functional Connectomes” - fMRI data describe functional connectivity); determining, based on the structural connectivity data and/or the functional connectivity data, at least one brain network connectivity measure associated with each of a plurality of brain regions (p.6-8: “dMRI…each pair of nodes are assigned by measuring the degree of white matter connectivity between the associated pair of ROIs”); and generating a multi-modal normative model that includes the at least one brain network connectivity measure associated with each of the plurality of brain regions (p.7: “tractography”).
Regarding claims 2, 3, and 22, Brown discloses that generating functional connectivity data comprises: extracting, within each of the plurality of brain regions, fMRI time series data; and computing, based on the extracted fMRI time series data, correlation coefficients between pairwise sets of regions of the plurality of regions, wherein the functional connectivity data includes the Pearson correlation coefficients for each region of the plurality of regions (p.8: “Functional connectivity can then be interpreted as communication between pairs of brain regions. The most standard measure of correlation is the Pearson’s correlation coefficient”).
Regarding claim 4, Brown discloses that generating structural connectivity data comprises: determining, based on the dMRI data, a number of fiber tracts connecting pairwise sets of regions of the plurality of regions, wherein the structural connectivity data includes the number of fiber tracts for each region of the plurality of regions (p.7: “dMRI”, “diffusion tensor imaging (DTI) model” produce fiber tracts for regions of the brain).
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 5-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Machine Learning on Human Connectome Data from MRI” by C.J. Brown et al. arXiv, 1611.08699, 2016 (Brown), as applied to claim 1 above, in view of “Brain Graphs: Graphical Models of the Human Brain Connectome” by E.T. Bullmore et al. Annu. Rev. Clin. Psychol. 7:113-40. 2011 (hereinafter as Bullmore, of record).
Regarding claim 5, Brown does not explicitly disclose that determining at least one brain network connectivity measure comprises: performing graph theoretic analysis on the structural connectivity data and/or the functional connectivity data to define a plurality of brain network connectivity measures associated with each of the plurality of brain regions. However, Bullmore teaches performing graphic theoretic analysis on connectivity data to define a plurality of brain networks (p.122: “To date, almost all graphical analyses of fMRI data have been based on a symmetric association matrix generated by some measure of functional connectivity between nodes”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the graphical analysis of Bullmore to the data of Brown, as to provide robust analysis of a connectome.
Regarding claim 6, Brown does not explicitly disclose thresholding the functional connectivity data to generate thresholded functional connectivity data; and binarizing the thresholded functional connectivity data to generate binarized functional connectivity data, wherein performing graph theoretic analysis on the functional connectivity data comprises performing graph theoretic analysis on the binarized functional connectivity data. However, Bullmore teaches using at least, a thresholded binary adjacency matrix towards graph analysis (p.124, Fig. 5). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the binarization and graph analysis of Bullmore to the data of Brown, as to provide robust analysis.
Regarding claim 7, Brown does not explicitly disclose thresholding the structural connectivity data to generate thresholded structural connectivity data; and binarizing the thresholded structural connectivity data to generate binarized structural connectivity data, wherein performing graph theoretic analysis on the structural connectivity data comprises performing graph theoretic analysis on the binarized structural connectivity data. However, Bullmore teaches using at least, a thresholded binary adjacency matrix towards graph analysis (p.124, Fig. 5). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the binarization and graph analysis of Bullmore to the data of Brown, as to provide robust analysis.
Regarding claim 8, Brown does not explicitly disclose that performing graph theoretic analysis comprises: computing at least one local topographical property of the structural connectivity data and/or the functional connectivity data. However, Bullmore teaches accounting for connection density (p.124). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the density consideration of Bullmore to the data of Brown, as to provide robust analysis.
Regarding claim 9, Brown does not explicitly disclose that the plurality of brain network connectivity measures includes one or more of degree. However, Bullmore teaches that connectivity measures are defined with respect to degrees (Fig. 5A shows degrees of correlation). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the association matrix of Bullmore to the data of Brown, as to provide robust analysis.
Regarding claim 10, Brown does not explicitly disclose that generating a multi-modal normative model comprises: normalizing the at least one brain network connectivity measure across the plurality of human subjects; and generating the multi-modal normative model based on the normalized at least one brain connectivity measure. However, a generally recited “normalizing” would have been an obvious step to one of ordinary skill in the art before the effective filing date of the present invention because it may merely amount to making any calculation which would put the claimed connectivity measure into a “normal” or “standard” state with respect to other associated data. Such a calculation would have been conventional and desirable to one with ordinary skill in the art in view of the inventor’s claims.
Claim(s) 11, 14, 15, 17, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Machine Learning on Human Connectome Data from MRI” by C.J. Brown et al. arXiv, 1611.08699, 2016 (Brown), as applied to claim 1 above, in view of “BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets” by R.V. Wael et al. Communications Biology. 3:103, 2020 (Wael, of record).
Regarding claims 11 and 31, Brown does not explicitly disclose that determining at least one brain network connectivity measure comprises: constructing, based on the structural connectivity data and/or the functional connectivity data, an affinity matrix for each subject of the plurality of human subjects; generating a set of gradients for each subject based, at least in part, on the affinity matrix; aligning the set of gradients for each subject to a group averaged template; and determining, for each subject and for each of the plurality of brain regions, a projection of each brain region onto each gradient, thereby determining the at least one brain network connectivity measure for each region of the plurality of regions. However, Wael teaches creating an affinity matrix and a set of gradients which are projected back to cortical surfaces of a brain model (Fig. 1). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the gradient identification of Wael to the data of Brown, as to provide robust analysis.
Regarding claim 14, Brown does not explicitly disclose that constructing the affinity matrix comprises using cosine similarity to construct the affinity matrix. However, Wael teaches that cosine similarity is used in the affinity computation (Fig. 1). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the cosine similarity of Wael to the data of Brown, as to provide robust analysis.
Regarding claim 15, Brown does not explicitly disclose that generating a set of gradients for each subject based, at least in part, on the affinity matrix comprises: reducing a dimensionality of the affinity matrix to derive a low dimensional manifold representation of the affinity matrix, wherein the set of gradients is generated based on the low dimensional manifold representation. However, Wael teaches reducing dimensionality in its calculation of gradients (Fig. 3, p.5: “different dimensionality reduction techniques”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the dimension reduction of Wael to the data of Brown, as to provide robust analysis.
Regarding claim 17, Brown does not explicitly disclose that the at least one brain network connectivity measure includes a value representing a component loading onto each gradient in the set of gradients. However, Wael teaches quantifying components which are to be projected onto a cortical surface of a brain model (Fig. 1: “First Components”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the component loading of Wael to the system of Brown, as to provide robust analysis.
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Machine Learning on Human Connectome Data from MRI” by C.J. Brown et al. arXiv, 1611.08699, 2016 (Brown) in view of “BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets” by R.V. Wael et al. Communications Biology. 3:103, 2020 (Wael, of record), as applied to claim 11 above, in view of “Brain Graphs: Graphical Models of the Human Brain Connectome” by E.T. Bullmore et al. Annu. Rev. Clin. Psychol. 7:113-40. 2011 (hereinafter as Bullmore, of record).
Regarding claim 12, neither Brown nor Wael explicitly disclose thresholding the functional connectivity data to generate thresholded functional connectivity data, wherein the constructing an affinity matrix based on the functional connectivity data comprises constructing the affinity matrix based on the thresholded functional connectivity data. However, Bullmore teaches using at least, a thresholded binary adjacency matrix towards graph analysis (p.124, Fig. 5). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the threshold of Bullmore to the data of Brown and Wael, as to provide robust computation.
Regarding claim 13, neither Brown nor Wael explicitly disclose thresholding the structural connectivity data to generate thresholded structural connectivity data, wherein the constructing an affinity matrix based on the structural connectivity data comprises constructing the affinity matrix based on the thresholded structural connectivity data. However, Bullmore teaches using at least, a thresholded binary adjacency matrix towards graph analysis (p.124, Fig. 5). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the threshold of Bullmore to the data of Brown and Wael, as to provide robust computation.
Conclusion
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/JASON M IP/Primary Examiner, Art Unit 3793