DETAILED 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 .
Election/Restrictions
The restriction requirement between group I and II, as set forth in the Office action mailed on 02/23/26, has been reconsidered in view of applicant’s arguments. The restriction requirement is hereby withdrawn. Specifically, the restriction requirement mailed on 02/23/26 is withdrawn. Claim 10, directed to a processor-implemented method is no longer withdrawn from consideration.
In view of the above noted withdrawal of the restriction requirement, applicant is advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Once a restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. See In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
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.
Claims 1, 3, and 5-10 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Leuthardt et al. (US 2019/0090749 A1).
With respect to claim 1, Leuthardt discloses a system for determining a plurality of networks of a brain of a subject from a resting state magnetic resonance imaging (MRI) data using a machine learning model for evaluating a plurality of health conditions of the subject, the system comprising: an imaging device that comprises at least one of a camera, or a screen, wherein the imaging device obtains an input data of the subject associated with an expert device that comprises at least one of scan data (see MRI system #102 as discussed in paragraph 0114), wherein the scan data comprises at least one of T1 weighted magnetic resonance imaging (MRI) image (see paragraph 0137), or a resting-state functional MRI image in a predefined format (see paragraphs 0093 and 0104); a brain network identifying server that acquires the input data of the subject from the imaging device (see paragraph 0112), and processes, the input data using the machine learning model, wherein the brain network identifying server comprises (see paragraph 220): a memory that stores a database ( database storage #110 in Figure 1 according to paragraph 0118); a processor that is configured to execute the machine learning model and is configured to, characterized in that, convert the predefined format of the scan data into an object format file by pre-processing the predefined format of the scan data of the subject; generate, using an independent component analysis method (see processor #214 in computer #104 as described in paragraphs 0122-0125), a four-dimensional (4D) functional connectivity file from the object format file, wherein the 4D functional connectivity file comprises at least one functional connectivity features (see paragraphs 0222 and 0269); decompose the 4D functional connectivity file into a n-component specified time-series, wherein the n-component specified time-series comprises time components that are independent of each other statistically (see paragraphs 0005 and 0140); train, using a plurality of data analysis pipelines, the machine learning model by providing a plurality of historical input data of historical subjects and a plurality of historical brain networks associated with the historical subjects as training data to obtain a trained machine learning model (see paragraphs 0145-0147 and 0220); obtain, using a multi-seed-based correlation analysis, a plurality of networks of the brain of the subject by providing a spatial relationship between a seed-region of the brain of the subject and rest of the object format file of the scan data of the subject when combined (see paragraphs 0100 and 0140-0147); compose the plurality of networks of the brain of the subject by assigning a defined threshold for a set of voxels of a set of seed-regions of the brain of the subject (see paragraphs 0100 and 0140-0142); determine, using an Intraoperative Direct Electrical Stimulation (DES) localization method and the trained machine learning model, the plurality of networks of the brain by comparing the plurality of composed networks of the brain with a template that defines a network of interest, wherein the brain network identifying server enables the evaluation of a plurality of health conditions of the subject using the plurality of networks that are determined (see paragraphs 0093-0094, 0101-0102, and 0112-0113).
With respect to claim 3, Leuthardt discloses the processor is configured to obtain a plurality of stable networks of the brain by implying a multi-seed-based correlation analysis by (i) combining the plurality of networks from the multiple seed-regions of the brain of the subject within a selected region of interest (ROI) (see paragraphs 0100 and 0140-0147) and (ii) weighing the multiple seed-region of the brain of the subject based on a distance from the main seed-region of the brain of the subject (see paragraph 0184).
With respect to claim 5, Leuthardt discloses the processor is configured to validate the plurality of networks of the brain by comparing the plurality of networks of the brain that are obtained based on tasks performed by the subject and correlating with the plurality of networks of the brain while the subject is performing a specified task using the interoperative DES localization method, wherein the plurality of networks of the brain obtained from task-based are the plurality of networks of the brain obtained from task-based (see paragraphs 0110-0113).
With respect to claim 6, Leuthardt discloses the interoperative DES localization method performs correlation to enable selection of the seed-region of the brain and comparison (see paragraphs 0100 and 0140-0147) between the Intraoperative Direct Electrical Stimulation activation and the plurality of networks of the brain (see paragraphs 0100-0101 and 0188).
With respect to claim 7, Leuthardt discloses the plurality of networks of the brain is at least one of a primary visual network and sensorimotor network of the brain, a language network, a dorsal default mode network of the brain, a posterior salience network, or a right executive control network of the brain (see paragraphs 0093, 0127 and 0185).
With respect to claim 8, Leuthardt discloses the predefined format of the scan data comprises at least one of digital imaging and communications in medicine format or neuroimaging informatics technology initiative format, wherein the seed-region of the brain of the subject comprises at least one voxel co-ordinate in the input data, wherein the rest of the object format file of the scan data of the subject is obtained by excluding the seed-region of the brain of the subject from the object format file of the scan data (see paragraphs 0143 and 0184).
With respect to claim 9, Leuthardt discloses the defined threshold is assigned using a statistical significance for the set of voxels given by the time components of the n-component specified time-series (see paragraphs 0100 and 0140-0142).
With respect to claim 10, Leuthardt discloses a processor-implemented method for determining a plurality of networks of a brain of a subject from a resting state magnetic resonance imaging (MRI) data using a machine learning model for evaluating a plurality of health conditions of the subject, wherein the method is performed by a system that comprises a brain network identifying server, the method comprising: obtaining, by the brain network identifying server of the system, the input data of the subject from an imaging device that comprises at least one of a camera, or a screen, wherein the input data of the subject associated with an expert device comprises at least one of scan data (see MRI system #102 as discussed in paragraph 0114), wherein the scan data comprises at least one of T1 weighted magnetic resonance imaging (MRI) image (see paragraph 0137), or a resting-state functional MRI image, wherein the scan data is in a predefined format (see paragraphs 0093 and 0104); characterized in that, converting, by a processor of the brain network identifying server, the predefined format of the scan data into an object format file by pre-processing the predefined format of the scan data of the subject (see paragraphs 0112 and 0122-0125); generating, by the processor, using an independent component analysis method, a four- dimensional (4D) functional connectivity file from the object format file, wherein the 4D functional connectivity file comprises at least one functional connectivity features (see paragraphs 0222 and 0269); decomposing, by the processor, the 4D functional connectivity file into a n-component specified time-series, wherein the n-component specified time-series comprises time components that are independent of each other statistically (see paragraphs 0005 and 0140); training, by the processor using a plurality of data analysis pipelines, the machine learning model by providing a plurality of historical input data of historical subjects and a plurality of historical brain networks associated with the historical subjects as training data to obtain a trained machine learning model (see paragraphs 0145-0147 and 0220); obtaining, by the processor, using a multi-seed-based correlation analysis, a plurality of networks of the brain of the subject by providing a spatial relationship between a seed-region of the brain of the subject and rest of the object format file of the scan data of the subject when combined (see paragraphs 0100 and 0140-0147); composing, by the processor, the plurality of networks of the brain of the subject by assigning a defined threshold for a set of voxels of a set of seed-regions of the brain of the subject (see paragraphs 0100 and 0140-0142); determining, by the processor, using an Intraoperative Direct Electrical Stimulation (DES) localization method and the trained machine Learning model, the plurality of networks of the brain by comparing the plurality of composed networks of the brain with a template that defines a network of interest, wherein the brain network identifying server enables the evaluation of a plurality of health conditions of the subject using the plurality of networks that are determined (see paragraphs 0093-0094, 0101-0102, and 0112-0113).
Allowable Subject Matter
Claims 2 and 4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIXOMARA VARGAS whose telephone number is (571)272-2252. The examiner can normally be reached Monday-Friday 8am-5pm.
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/DIXOMARA VARGAS/Primary Examiner, Art Unit 3798
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