CTNF 19/195,070 CTNF 95055 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims This action is in reply to the claims filed on 30 April 2025. Claims 1-29 are currently pending and have been examined. Claim Objections 07-29-01 AIA Claim 6 is objected to because of the following informalities: there is a second sentence starting in line 5 of the claim. “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations” (see MPEP 608.01(m)) . Appropriate correction is required. 07-29-01 AIA Claim 7 is objected to because of the following informalities: there is a second sentence starting in line 5 of the claim. “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations” (see MPEP 608.01(m)) . Appropriate correction is required. 07-29-01 AIA Claim 27 is objected to because of the following informalities: for the purposes of clarity, the first use of the abbreviation “VNN” (in line 2 of independent claim 27) should be spelled out in full . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 6-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. 07-34-08 Regarding claims 6 and 7 , the phrase "Example of" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Appropriate correction is required. Regarding claim 8 , the phrase "the subject may have" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-29 are rejected under 35 USC § 101 Step 1 : Is the claim to a process, machine, manufacture, or composition of matter? Claims 1-29 fall within one or more statutory categories. Claims 1-15 fall within the category of a process. Claims 16-26 fall within the category of a machine. Claims 27-29 fall within the category of a manufacture. Step 2A Prong One : Does the claim recite an abstract idea, law of nature, or natural phenomenon? Claims 1-29 recite an abstract idea. Representative claim 1 recites: … predict features informative of chronological age using an anatomical covariance matrix and brain anatomical data derived from a population comprising a largest portion of healthy subjects; providing … brain anatomical data of a subject and the anatomical covariance matrix; generating … a set of biomarkers indicative of neurodegeneration of the subject; generating, based on the set of biomarkers …, a brain health marker indicative of neurodegeneration of the subject. Therefore, the claim as a whole is directed to “diagnosing neurodegeneration” which is an abstract idea because it is a of organizing human activity. “Diagnosing neurodegeneration” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims includes the interaction between a healthcare provider and a patient through the patient’s neural imaging. Alternatively, the elements above are directed to a mental process because they include concepts performed in the human mind (including an observation, evaluation, judgment, opinion). This includes the observation and evaluation of the patient data. Step 2A Prong Two : Does the claim recite additional elements that integrate the judicial exception into a practical application? This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): providing and using a trained VNN. The additional elements individually or in combination do not integrate the exception into a practical application. The additional elements merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea. Step 2B : Does the claim recite additional elements that amount to significantly more than the judicial exception? Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible. Dependent claim 2 recites the method of claim 1, wherein: the brain anatomical data used to train the VNN comprises multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions, and is derived from at least one of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains of a population comprising healthy subjects. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 2 is considered to be ineligible. Dependent claim 3 recites the method of claim 1, wherein: the VNN is trained for predicting chronological age or features informative of the chronological age. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible. Dependent claim 4 recites the method of claim 1, wherein: the brain anatomical data of the subject comprises a multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions and is derived from a combination of at least two of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 4 is considered to be ineligible. Dependent claim 5 recites the method of claim 1, wherein: the brain anatomical data of the subject captures information about the same section of the brain as the brain anatomical data used to train the VNN. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 5 is ineligible. Dependent claim 6 recites the method of claim 1, wherein: elements of the anatomical covariance matrix used to train the VNN are determined by covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible. Dependent claim 7 recites the method of claim 1, wherein: elements of the anatomical covariance matrix used to process the brain anatomical data of the subject are determined by the covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 7 is ineligible. Dependent claim 8 recites the method of claim 1, wherein: the anatomical covariance matrix used to process the brain anatomical data of the subject may have different number of features relative to the anatomical covariance matrix used to train the VNN. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 8 is ineligible. Dependent claim 9 recites the method of claim 1, wherein: generating the biomarker indicative of neurodegeneration of the subject includes generating the biomarker as outputs of the VNN or statistical transformation of the outputs of the VNN. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 9 is ineligible. Dependent claim 10 recites the method of claim 1, wherein: generating the brain health marker indicative of neurodegeneration of the subject comprises determining, from the biomarkers, a prediction of brain age of the subject. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 10 is considered to be ineligible. Dependent claim 11 recites the method of claim 1, wherein: generating the brain health marker indicative of neurodegeneration of the subject comprises determining, from the biomarkers, a label of the subject among a category representing healthy population and one or more categories representing populations with neurodegenerative health conditions. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 11 is considered to be ineligible. Dependent claim 12 recites the method of claim 1, comprising: mapping anatomical regions of the subject's brain to the biomarker and identifying anatomical regions of the subject's brain contributing to brain age of the subject. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 12 is considered to be ineligible. Dependent claim 13 recites the method of claim 12, wherein: identifying the anatomical regions contributing to the brain age includes evaluating a statistic for an anatomical region from the biomarker that characterizes the anatomical region with respect to the brain age determined from the biomarkers. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 13 is considered to be ineligible. Dependent claim 14 recites the method of claim 13, wherein: identifying the anatomical regions of the subject's brain contributing to the brain age comprises identifying the anatomical regions contributing to a prediction of brain age of the subject. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 14 is considered to be ineligible. Dependent claim 15 recites the method of claim 12, wherein: identifying the anatomical regions contributing to the brain age includes statistically comparing the biomarker for the subject relative to the biomarkers of a healthy population The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 15 is ineligible. Independent claim 16 recites a system that is substantially similar to the method of claim 1. However, claim 16 recites the following additional elements: a computing platform including at least one processor, a memory. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 16 is ineligible. Dependent claim 17 recites the system of claim 16, wherein: the brain anatomical data used to train the VNN comprises a multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions, and is derived from a combination of two or more of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains of a population comprising healthy subjects. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 17 is considered to be ineligible. Dependent claim 18 recites the system of claim 16, wherein: elements of an anatomical covariance matrix used to train the VNN are determined by covariance between features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 18 is ineligible. Dependent claim 19 recites the system of claim 16, wherein: generating the set of biomarkers indicative of neurodegeneration of the subject includes generating the set as outputs of the VNN or a statistical transformation of the outputs of the VNN. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 19 is ineligible. Dependent claim 20 recites the system of claim 16, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes a linear or non-linear transformation of the biomarkers. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 20 is considered to be ineligible. Dependent claim 21 recites the system of claim 16, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes generating brain age based on a linear or non-linear aggregation of the biomarkers. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 21 is considered to be ineligible. Dependent claim 22 recites the system of claim 16, comprising: identifying anatomical regions of the subject's brain contributing to brain age of the subject by evaluating a residual vector for each anatomical region from the biomarker generated by the VNN that characterizes the anatomical region. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 22 is ineligible. Dependent claim 23 recites the system of claim 16, comprising: mapping anatomical regions of the subject's brain to the biomarkers. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 23 is considered to be ineligible. Dependent claim 24 recites the system of claim 16, comprising: identifying anatomical regions of the subject's brain contributing to the brain health marker. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 24 is considered to be ineligible. Dependent claim 25 recites the system of claim 16, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes prediction of the subject or the brain of the subject being healthy or unhealthy. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 25 is considered to be ineligible. Dependent claim 26 recites the system of claim 16, comprising: identifying anatomical regions of the subject's brain contributing to prediction of the subject or the brain of the subject being healthy or unhealthy. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 26 is considered to be ineligible. Independent claim 27 recites a non-transitory computer readable medium that stores a method substantially similar to the methos of claim 1. However, claim 27 recites the following additional elements: executable instructions that when executed by at least one processor of at least one computer cause the at least one computer to perform steps comprising: [the method of claim 1]. The additional elements present in this claim merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 27 is ineligible. Dependent claim 28 recites the medium of claim 27, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes generating a prediction of brain age of the subject. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 28 is considered to be ineligible. Dependent claim 29 recites the medium of claim 27, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes predicting the subject or the brain of the subject as healthy or unhealthy. This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 29 is considered to be ineligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-3, 5-16, and 18-29 are rejected under 35 U.S.C. 103 as being unpatentable over Siemionow et al. (U.S. 2020/0357119), hereinafter “Siemionow,” in view of Sihag, Saurabh, et al., “CoVariance Neural Networks,” Advances in Neural Information Processing Systems, vol. 35, pp. 17003–16, 31 Oct 2022, hereinafter “Sihag.” Regarding Claim 1 , Siemionow discloses a method for identifying biomarkers indicative of neurodegeneration using a covariance neural network (VNN), the method comprising: providing a [neural network] trained to predict features informative of chronological age (See Siemionow [0043] the system uses a neural network to determine brain age.) … brain anatomical data derived from a population comprising a largest portion of healthy subjects (See Siemionow [0003] the system trains models on healthy individuals in order to make brain-based predictions of age.) ; providing, as input to the [neural network], brain anatomical data of a subject … (See Siemionow [0054] Once the training process is complete, the network can be used for inference (i.e., utilizing a trained model for autonomous determination of the brain age). The model can be saved and reused, training doesn't need to be performed before each use.) ; generating, by the [neural network] and based on the input, a set of biomarkers indicative of neurodegeneration of the subject (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) ; and generating, based on the set of biomarkers generated by the [neural network], a brain health marker indicative of neurodegeneration of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN); [training a neural network] using an anatomical covariance matrix; [using as input into the neural network] the anatomical covariance matrix. Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) ; [training a neural network] using an anatomical covariance matrix (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) ; [using as input into the neural network] the anatomical covariance matrix (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 2 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: the brain anatomical data used to train the [neural network] comprises multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions (See Siemionow [0044] The network is configured to accept as a primary input a set of T1-weighted and T2-weighted volumes of the brain to be analyzed, preferably provided as a multichannel 3D volume. In addition, the primary input may further comprise volumes of other types, such as dark-fluid T2-weighted or diffusion weighted volumes or their variations with contrast addition.) , and is derived from at least one of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains of a population comprising healthy subjects (See Siemionow [0030] the system uses MRI imaging data as input for the neural network.) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 3 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: The [neural network] is trained for predicting chronological age or features informative of the chronological age (See Siemionow [0043] the system uses a neural network to determine brain age.) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 5 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: the brain anatomical data of the subject captures information about the same section of the brain as the brain anatomical data used to train the [neural network] (See Siemionow [0047] The training database may comprise a plurality of sets of at least the T1-weighted and T2-weighted scans of healthy individuals with their biological age. This is the same sections and scans as the input for the model disclosed in [0044].) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 6 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow does not further disclose a method, wherein: elements of the anatomical covariance matrix used to train the VNN are determined by covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication. Sihag teaches: elements of the anatomical covariance matrix used to train the VNN are determined by covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 7 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow does not further disclose a method, wherein: elements of the anatomical covariance matrix used to process the brain anatomical data of the subject are determined by the covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication. Sihag teaches: elements of the anatomical covariance matrix used to process the brain anatomical data of the subject are determined by the covariance between the features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Examples of transformations on the covariance between the features associated with different brain regions can include thresholding, division, normalization, and multiplication (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 8 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow does not further disclose a method, wherein: the anatomical covariance matrix used to process the brain anatomical data of the subject may have different number of features relative to the anatomical covariance matrix used to train the VNN. Sihag teaches: the anatomical covariance matrix used to process the brain anatomical data of the subject may have different number of features relative to the anatomical covariance matrix used to train the VNN (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions. Different resolutions are different features. See also section E.5) - (Further, Examiner notes that this language(i.e. “may”) is considered to be intended use and is not given patentable weight.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 9 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: generating the biomarker indicative of neurodegeneration of the subject includes generating the biomarker as outputs of the [neural network] or statistical transformation of the outputs of the [neural network] (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 10 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: generating the brain health marker indicative of neurodegeneration of the subject comprises determining, from the biomarkers, a prediction of brain age of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 11 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: generating the brain health marker indicative of neurodegeneration of the subject comprises determining, from the biomarkers, a label of the subject among a category representing healthy population and one or more categories representing populations with neurodegenerative health conditions (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] localizing the brain part that influenced a prediction of the brain age that significantly differs from the biological age may directly indicate where to look for abnormalities and degenerations, which in turn enables more informed and faster diagnosis.) . Regarding claim 12 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, comprising: mapping anatomical regions of the subject's brain to the biomarker (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) and identifying anatomical regions of the subject's brain contributing to brain age of the subject (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 13 , Siemionow in view of Sihag discloses the method of claim 12 as discussed above. Siemionow further discloses a method, wherein: identifying the anatomical regions contributing to the brain age includes evaluating a statistic for an anatomical region from the biomarker that characterizes the anatomical region with respect to the brain age determined from the biomarkers (See Siemionow [0076] The set of occluded scans is input the neural network and the inference result (i.e. the determined brain age for the occluded set) is compared with the initially determined brain age. If the difference is significant, i.e. higher than a predetermined threshold, that occluded area is marked as an area that is significant for the determination of the brain age. If not, that occluded area is marked as not significant. More threshold levels may be also defined, for determining different levels of significance.) . Regarding claim 14 , Siemionow in view of Sihag discloses the method of claim 13 as discussed above. Siemionow further discloses a method, wherein: identifying the anatomical regions of the subject's brain contributing to the brain age comprises identifying the anatomical regions contributing to a prediction of brain age of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 15 , Siemionow in view of Sihag discloses the method of claim 12 as discussed above. Siemionow further discloses a method, wherein: identifying the anatomical regions contributing to the brain age includes statistically comparing the biomarker for the subject relative to the biomarkers of a healthy population (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0076] The set of occluded scans is input the neural network and the inference result (i.e. the determined brain age for the occluded set) is compared with the initially determined brain age. If the difference is significant, i.e. higher than a predetermined threshold, that occluded area is marked as an area that is significant for the determination of the brain age. If not, that occluded area is marked as not significant. More threshold levels may be also defined, for determining different levels of significance.) . Regarding claim 16 , Siemionow discloses a system for identifying biomarkers indicative of neurodegeneration using a covariance neural network (VNN), the system comprising: a computing platform including at least one processor, a memory (See Siemionow [0017] the system includes the use of memory and processors.) , and a [neural network] trained exclusively on brain anatomical data from a dataset comprising brain anatomical derived from a population comprising a largest portion of healthy subjects (See Siemionow [0003] the system trains models on healthy individuals in order to make brain-based predictions of age.) , with the [neural network] implemented by the at least one processor for: receiving brain anatomical data of a subject as input (See Siemionow [0054] Once the training process is complete, the network can be used for inference (i.e., utilizing a trained model for autonomous determination of the brain age). The model can be saved and reused, training doesn't need to be performed before each use.) ; generating, based on the input, a set of biomarkers indicative of neurodegeneration of the subject (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) ; and generating, based on the set of biomarkers, a brain health marker indicative of neurodegeneration of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 18 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow does not further disclose, wherein: elements of an anatomical covariance matrix used to train the VNN are determined by covariance between features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions. Sihag teaches: elements of an anatomical covariance matrix used to train the VNN are determined by covariance between features associated with different brain regions or a transformation of the covariance between the features associated with different brain regions (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 19 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, wherein: generating the set of biomarkers indicative of neurodegeneration of the subject includes generating the set as outputs of the [neural network] or a statistical transformation of the outputs of the [neural network] (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 20 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow does not further disclose a system, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes a linear or non-linear transformation of the biomarkers. Sihag teaches: generating the brain health marker indicative of neurodegeneration of the subject includes a linear or non-linear transformation of the biomarkers (See Sihag page 8, section 5.1, subsection titled “PCA-regression; the system uses a pipeline that includes linear regression to transform the dataset of brain data for use with the covariance neural network.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 21 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow does not further disclose a system, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes generating brain age based on a linear or non-linear aggregation of the biomarkers. Sihag teaches: generating the brain health marker indicative of neurodegeneration of the subject includes generating brain age based on a linear or non-linear aggregation of the biomarkers (See Sihag page 8, section 5.1, subsection titled “PCA-regression; the system uses a pipeline that includes linear regression to transform the dataset of brain data for use with the covariance neural network.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 22 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, comprising: identifying anatomical regions of the subject's brain contributing to brain age of the subject by evaluating a residual vector for each anatomical region from the biomarker generated by the [neural network] that characterizes the anatomical region (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].). See also [0050] for the use of a loss function during training.) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 23 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, comprising: mapping anatomical regions of the subject's brain to the biomarkers (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) . Regarding claim 24 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, comprising: identifying anatomical regions of the subject's brain contributing to the brain health marker (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 25 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes prediction of the subject or the brain of the subject being healthy or unhealthy (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] localizing the brain part that influenced a prediction of the brain age that significantly differs from the biological age may directly indicate where to look for abnormalities and degenerations, which in turn enables more informed and faster diagnosis.) . Regarding claim 26 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, comprising : identifying anatomical regions of the subject's brain contributing to prediction of the subject or the brain of the subject being healthy or unhealthy (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 27 , Siemionow discloses a non-transitory computer readable medium having stored thereon: a [neural network] trained exclusively on brain anatomical data derived from a population comprising a largest portion of healthy subjects (See Siemionow [0003] the system trains models on healthy individuals in order to make brain-based predictions of age.) and executable instructions that when executed by at least one processor of at least one computer (See Siemionow [0017] the system includes the use of memory and processors.) cause the at least one computer to perform steps comprising: receiving brain anatomical data of a subject as input (See Siemionow [0054] Once the training process is complete, the network can be used for inference (i.e., utilizing a trained model for autonomous determination of the brain age). The model can be saved and reused, training doesn't need to be performed before each use.) ; generating, based on the input, a set of biomarkers indicative of neurodegeneration of the subject (See Siemionow [0076] the system is used to mark areas that is significant for the determination of the brain age. This is understood to be a biomarkers indicative of neurodegeneration. [0002] the system is used for identifying biomarkers for improving the detection of early-stage neurodegeneration and predict cognitive decline related to age.) ; and generating, based on the input, a brain health marker indicative of neurodegeneration of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Siemionow does not disclose: [the neural network is a] covariance neural network (VNN). Sihag teaches: [the neural network is a] covariance neural network (VNN) (See Sihag page 7, section 5; this paper discusses the use of a VNN for determining chronological age based on sample data of health individuals. page 21, section E.2, the brain data used is MRI data. Page 8, section 5.1, the VNN uses a covariance matrix made up of cortical thickness at different resolutions.) . The system of Sihag is applicable to the disclosure of Siemionow as they both share characteristics and capabilities, namely, they are directed to evaluating patient brain data using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include the use of covariance matrices and VNNs as taught by Sihag. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow because cortex anatomical measures extracted from structural MRI scans have shown promising results in age prediction in existing studies (see Sihag page 21; section E.1). Regarding claim 28 , Siemionow in view of Sihag discloses the medium of claim 27 as discussed above. Siemionow further discloses a medium, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes generating a prediction of brain age of the subject (See Siemionow [0077] the system determines a brain age (i.e. “a brain health marker”) based on the results of the neural network inference. This includes the identification of biomarker areas of the brain that contribute to the brain age as disclosed in [0076].) . Regarding claim 29 , Siemionow in view of Sihag discloses the medium of claim 27 as discussed above. Siemionow further discloses a medium, wherein: generating the brain health marker indicative of neurodegeneration of the subject includes predicting the subject or the brain of the subject as healthy or unhealthy (See Siemionow [0074] In case the determined brain age deviates from the patient's age, then apart from simply presenting the result of the inference as a value of the determined brain age, a further process may be performed to determine which areas of the brain contributed to the result. [0077] localizing the brain part that influenced a prediction of the brain age that significantly differs from the biological age may directly indicate where to look for abnormalities and degenerations, which in turn enables more informed and faster diagnosis.) . 07-21-aia AIA Claim s 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Siemionow et al. (U.S. 2020/0357119), hereinafter “Siemionow,” in view of Sihag, Saurabh, et al., “CoVariance Neural Networks,” Advances in Neural Information Processing Systems, vol. 35, pp. 17003–16, 31 Oct 2022, hereinafter “Sihag,” and further in view of Kamali-Zare et al. (U.S. 2018/0268942), hereinafter “Kamali-Zare.” Regarding claim 4 , Siemionow in view of Sihag discloses the method of claim 1 as discussed above. Siemionow further discloses a method, wherein: the brain anatomical data of the subject comprises a multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions (See Siemionow [0044] The network is configured to accept as a primary input a set of T1-weighted and T2-weighted volumes of the brain to be analyzed, preferably provided as a multichannel 3D volume. In addition, the primary input may further comprise volumes of other types, such as dark-fluid T2-weighted or diffusion weighted volumes or their variations with contrast addition.) . Siemionow does not disclose: [the brain anatomical data] is derived from a combination of at least two of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains. Kamali-Zare teaches: [the brain anatomical data] is derived from a combination of at least two of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains (See Kamali-Zare [0126] the system uses neural networks to analyze a combination of at least MRI, CT and PET imaging. See abstract, this analysis is used for determining whether brain tissue is indicative of a neurodegenerative disorder.) . The system of Kamali-Zare is applicable to the disclosure of Siemionow in vie of Sihag as they both share characteristics and capabilities, namely, they are directed to analyzing brain imaging for signs of neurological degeneration. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include a combination of imaging methodologies as taught by Kamali-Zare. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow in order to leverage a deeper understanding of brain tissue microstructure to more reliably predict and interpret the health of the brain from brain scans well before severe tissue damage irreversibly impedes healthy cognitive function (see Kamali-Zare [0005]). Regarding claim 17 , Siemionow in view of Sihag discloses the system of claim 16 as discussed above. Siemionow further discloses a system, wherein: the brain anatomical data used to train the [neural network] comprises a multivariate dataset, whose each element captures a characteristic of a brain region or a combination of brain regions (See Siemionow [0044] The network is configured to accept as a primary input a set of T1-weighted and T2-weighted volumes of the brain to be analyzed, preferably provided as a multichannel 3D volume. In addition, the primary input may further comprise volumes of other types, such as dark-fluid T2-weighted or diffusion weighted volumes or their variations with contrast addition.) . Siemionow does not disclose: [the brain anatomical data] is derived from a combination of two or more of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains of a population comprising healthy subjects. Kamali-Zare teaches: [the brain anatomical data] is derived from a combination of two or more of: magnetic resonance imaging (MRI) images, computed tomography (CT) scan, positron emission tomography (PET) scan, electroencephalogram (EEG) test of brains of a population comprising healthy subjects (See Kamali-Zare [0126] the system uses neural networks to analyze a combination of at least MRI, CT and PET imaging. See abstract, this analysis is used for determining whether brain tissue is indicative of a neurodegenerative disorder.) . The system of Kamali-Zare is applicable to the disclosure of Siemionow in view of Sihag as they both share characteristics and capabilities, namely, they are directed to analyzing brain imaging for signs of neurological degeneration. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Siemionow to include a combination of imaging methodologies as taught by Kamali-Zare. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Siemionow in order to leverage a deeper understanding of brain tissue microstructure to more reliably predict and interpret the health of the brain from brain scans well before severe tissue damage irreversibly impedes healthy cognitive function (see Kamali-Zare [0005]) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Besson et al. (U.S. 2022/0122250) teaches a system and method for brain feature prediction using geometric deep learning on graph representations of medical image data . Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN L HANKS whose telephone number is (571)270-5080. The examiner can normally be reached Monday-Friday 8am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.L.H./Examiner, Art Unit 3684 /KENNETH BARTLEY/Primary Examiner, Art Unit 3684 Application/Control Number: 19/195,070 Page 2 Art Unit: 3684 Application/Control Number: 19/195,070 Page 3 Art Unit: 3684 Application/Control Number: 19/195,070 Page 4 Art Unit: 3684 Application/Control Number: 19/195,070 Page 5 Art Unit: 3684 Application/Control Number: 19/195,070 Page 7 Art Unit: 3684 Application/Control Number: 19/195,070 Page 9 Art Unit: 3684 Application/Control Number: 19/195,070 Page 10 Art Unit: 3684 Application/Control Number: 19/195,070 Page 11 Art Unit: 3684 Application/Control Number: 19/195,070 Page 12 Art Unit: 3684 Application/Control Number: 19/195,070 Page 13 Art Unit: 3684 Application/Control Number: 19/195,070 Page 14 Art Unit: 3684 Application/Control Number: 19/195,070 Page 15 Art Unit: 3684 Application/Control Number: 19/195,070 Page 16 Art Unit: 3684 Application/Control Number: 19/195,070 Page 17 Art Unit: 3684 Application/Control Number: 19/195,070 Page 18 Art Unit: 3684 Application/Control Number: 19/195,070 Page 19 Art Unit: 3684 Application/Control Number: 19/195,070 Page 20 Art Unit: 3684 Application/Control Number: 19/195,070 Page 21 Art Unit: 3684 Application/Control Number: 19/195,070 Page 22 Art Unit: 3684 Application/Control Number: 19/195,070 Page 23 Art Unit: 3684 Application/Control Number: 19/195,070 Page 24 Art Unit: 3684 Application/Control Number: 19/195,070 Page 25 Art Unit: 3684 Application/Control Number: 19/195,070 Page 26 Art Unit: 3684 Application/Control Number: 19/195,070 Page 27 Art Unit: 3684 Application/Control Number: 19/195,070 Page 28 Art Unit: 3684 Application/Control Number: 19/195,070 Page 29 Art Unit: 3684 Application/Control Number: 19/195,070 Page 30 Art Unit: 3684 Application/Control Number: 19/195,070 Page 32 Art Unit: 3684 Application/Control Number: 19/195,070 Page 33 Art Unit: 3684 Application/Control Number: 19/195,070 Page 34 Art Unit: 3684 Application/Control Number: 19/195,070 Page 35 Art Unit: 3684 Application/Control Number: 19/195,070 Page 37 Art Unit: 3684 Application/Control Number: 19/195,070 Page 38 Art Unit: 3684 Application/Control Number: 19/195,070 Page 40 Art Unit: 3684 Application/Control Number: 19/195,070 Page 41 Art Unit: 3684 Application/Control Number: 19/195,070 Page 43 Art Unit: 3684 Application/Control Number: 19/195,070 Page 44 Art Unit: 3684 Application/Control Number: 19/195,070 Page 45 Art Unit: 3684
Prosecution Insights