earchDETAILED 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 rejected under 35 U.S.C. 101 because the claimed invention is directed to a computer-aided system for diagnosing autism spectrum disorder (ASD) without significantly more.
Claims 1, 13, The claim(s) recite(s):
receiving neuroimaging data of a subject brain;
parcellating the neuroimaging data of the subject brain into a plurality of brain regions according to a brain atlas;
extracting a plurality of quantitative metrics of the subject brain from the neuroimaging data of each brain region;
identifying correlations between the extracted plurality of quantitative metrics from different brain regions;
determining which of the plurality of brain regions are associated with ASD based at least in part on the identified correlations;
determining which of the brain regions associated with ASD are associated with each of a plurality of assessment modules; and
classifying, for each of the plurality of assessment modules, a severity of ASD for that assessment module based at least in part on the identified correlations in brain regions associated with ASD and associated with that assessment module.
This judicial exception is not integrated into a practical application because there are concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of lack of structures and could be done mentally
The processor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. This determining step, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (one or more processors and computer storage memory)
That is, other than reciting "by a processor", nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. The additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component which cannot integrate a judicial exception into a practical application. The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
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, 4-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Emerson et al (Pub. No.: US 2019/0133446)
Regarding claims 1, 14, Emerson et al disclose a computer-aided system for diagnosing autism spectrum disorder (ASD),
the system comprising:
at least one non-transitory computer-readable storage medium having computer program instructions stored thereon;
at least one processor configured to execute the computer program instructions causing the processor to perform the following operations:
receiving neuroimaging data of a subject brain [see 0087-0092];
parcellating the neuroimaging data of the subject brain into a plurality of brain regions according to a brain atlas [see 0063];
extracting a plurality of quantitative metrics (scores) of the subject brain from the neuroimaging data of each brain region [see 0028-0029, 0033, 0051-0052, 0084];
identifying correlations between the extracted plurality of quantitative metrics from different brain regions [see 0069-0070, 0094];
determining which of the plurality of brain regions are associated with ASD based at least in part on the identified correlations [see 0094];
determining which of the brain regions associated with ASD are associated with each of a plurality of assessment modules [see 0094];
classifying, for each of the plurality of assessment modules, a severity of ASD for that assessment module based at least in part on the identified correlations in brain regions associated with ASD and associated with that assessment module [see 0029, 0041-0042, 0068-0071, 0094].
Regarding claim 4, Emerson et al disclose wherein the step of determining which of the plurality of brain regions are associated with ASD based at least in part on the identified correlations includes an initial step of univariate feature selection to omit less relevant identified correlations followed by a subsequent step of using a machine learning technique to identify a subset of identified correlations as characteristic of ASD from the identified correlations not omitted in the initial step [see 0029, 0040-0041, 0094].
Regarding claim 6, Emerson et al disclose wherein the step of classifying, for each of the plurality of assessment modules, a severity of ASD for that assessment module is performed using a plurality of machine learning classifiers, each trained to distinguish between different severities of ASD and typical development specific to a different assessment module [see 0041-0042, 0068-0071].
Regarding claim 7, Emerson et al disclose wherein each of the plurality of machine learning classifiers are trained on the subset of identified correlations [see 0041-0042, 0068-0071].
Regarding claim 8, Emerson et al disclose wherein each of the plurality of machine learning classifiers are one of a logistic regression classifier, a linear support vector machine, a gradient boosting classifier, and a k-nearest neighbor classifier [see 0068].
Regarding claim 9, Emerson et al disclose wherein the classifying the severity of ASD for each of the plurality of assessment modules includes classifying the severity of ASD as one of typical development, mild ASD, moderate ASD or severe ASD (high risk) for each of the plurality of assessment modules [see 0041-0042, 0068-0071].
Regarding claim 10, Emerson et al disclose wherein the at least one processor is configured to execute the computer program instructions causing the processor to perform the following additional operation:
generating a final diagnosis based at least in part on the classification of the severity of ASD for each of the plurality of assessment modules, wherein the final diagnosis is either autism spectrum disorder or typical development [see 0087-0092].
Regarding claim 11, Emerson et al disclose generating, using a machine learning classifier, a final diagnosis based at least in part on the classification of the severity of ASD for each of the plurality of assessment modules, wherein the final diagnosis is either autism spectrum disorder or typical development, and wherein the machine learning classifier is trained on the subset of identified correlations [see 0040-0041, 0068-0071, 0087-0092].
Regarding claim 12, Emerson et al disclose identifying correlations between the extracted plurality of quantitative metrics from different brain regions includes identifying correlations in statistical properties of water diffusion within each brain region.
Regarding claim 14, Emerson et al disclose generating a graphical visualization of the classification for each assessment module [see 0091].
Regarding claim 15, Emerson et al disclose generating a final diagnosis based at least in part on the classification of the severity of ASD for each of the plurality of assessment modules, wherein the final diagnosis is either autism spectrum disorder or typical development [see 0040-0041, 0068-0071, 0087-0092].
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 (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 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) 2-3, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Emerson et al (Pub. No.: US 2019/0133446) in view of Chance et al (Pub. No.: US 2018/0145282).
Regarding claims 2-3, 16-18, Emerson et al don’t disclose identifying correlations between the extracted plurality of quantitative metrics from different brain regions includes identifying correlations in statistical properties of water diffusion within each brain region;
wherein the neuroimaging data of the subject brain is diffusion tensor imaging data of the subject brain;
wherein the quantitative metrics comprise at least one of fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, and TSkew.
Nonetheless, Chance et al disclose identifying correlations between the extracted plurality of quantitative metrics from different brain regions includes identifying correlations in statistical properties of water diffusion within each brain region [see 0062-0068]
wherein the neuroimaging data of the subject brain is diffusion tensor imaging data of the subject brain [see 0062-0068];
wherein the quantitative metrics comprise at least one of fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, and TSkew [see 0062-0068].
Therefore, it is obvious to one skilled in the art at the time the invention was filed and would have been motivated to combine Emerson et al and Chance et al; Its ability to detect directional (anisotropic) diffusion makes it a powerful tool for visualizing and quantifying the integrity and organization of neural pathways.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Emerson et al (Pub. No.: US 2019/0133446) in view of West et al (Pub. No.: US 2016/0169915)
Regarding claim 5, Emerson et al don’t disclose wherein the machine learning technique is recursive feature elimination.
Nonetheless, West et al disclose wherein the machine learning technique is recursive feature elimination [see 0098].
Therefore, it is obvious to one skilled in the art at the time the invention was filed and would have been motivated to combine Emerson et al and West et al by using recursive feature elimination; Reduces Overfitting by removing irrelevant or noisy features, the model generalizes better to unseen data and Increases Accuracy by Keeping only the most informative features can improve predictive performance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL F BRUTUS whose telephone number is (571)270-3847. The examiner can normally be reached Mon-Sat, 11:00 AM to 7:00 PM.
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/JOEL F BRUTUS/ Primary Examiner, Art Unit 3797
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