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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/07/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5 and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature (NPL): “A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression” to Riva-Posse et al. “Riva-Posse”, in view of US2020/0225308 to Dosenbach et al. “Dosenbach”, and further in view of US2023/0419484 to “Moreno”.
Regarding claim 1, Riva-Posse discloses a computer-implemented method (See supplemental material section, Page S1, image processing of the image data were performed using software such as FSL; Page S2, DBS modeling for generation of probabilistic tractography maps were generated using deep brain stimulation neurosurgical research software; the method disclosed by Riva-Posses uses various software and thus would read on a computer-implemented method) for brain mapping (probabilistic tract map, Abstract) and target identification (Page 2, left column, “identify the SCC DS surgical target; Page 4, right column, “Discussion”, method for SCC DBS targeting) for interventional planning (Page 2, left column, Riva-Posse suggest the method can be used to guide surgical implantation and contact selection for chronic stimulation) using magnetic resonance imaging (MRI) (Page 2, right column, “Pre-surgical planning”), the method comprising:
receiving, by a computing system that includes at least one processor in communication with at least one memory system and that is in communication to receive data acquired using an MRI system (Page S1, all images were preprocessed using tools from FSL software, wherein the images are diffusion data with T1 images, which would read on MRI data; therefore it is inferred and also common knowledge that the imaging processing software, such as FSL, would run on a computing device such as a general purpose computer, wherein the computer would have at least one processor in communication with at least one memory system, and is in communication to receive data acquired using an MRI system) MR data from the MRI system (High-resolution T1 and diffusion-weighted images were collected within a single session for each subject on a research-dedicated Siemens 3T Tim-Trio scanner, Page 2, right column, “Magnetic resonance image acquisition and pre-processing.”);
generating, by the computer system, a map of the subject's brain based on the set of useable MR data (Page 2, right column, “prospective patient-specific target selection”, generation of the blueprint on each patient’s own MRI-DTI scan, wherein Fig. 2a show’s an exemplary blueprint, with the four fiber bundles overlayed on the brain MRI image); and
identifying, by the computing system, a target location in the subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain (Fig. 2b, individualized deterministic tractography target selection in one subject, which shows the optimal target location within SCC region with modeled stimulation impacting necessary fiber bundles for effective SCC DBS; wherein Fig. 2b is generated based by determining the target location that best visually matched the blueprint as shown in Fig. 2a) , wherein the target location is a point of convergence of multiple fiber bundles passing through the SCC region (as can be seen in Fig. 2b, multiple fiber bundles CB, FM, F-st, and UF converges around the blue sphere, through the SCC region).
However, Riva-Posse does not explicitly disclose analyzing, by the computer system, the received MR data to monitor and identify motion in real-time, and determining, by the computing system, a set of useable MR data from the acquired MR data based on the identified motion.
Dosenbach teaches analyzing, by the computer system, the received MR data to monitor and identify motion in real-time (Title, “Real time monitoring and prediction of motion in mri”; Paragraph 0110, method, system and device for real-time monitoring of motion of a body part of a patient during MRI scanning, wherein the head is provided as an example of the body part; Paragraph 0160, wherein the method is performed using a computing system including one or more computers) and determining, by the computing system, a set of useable MR data from the acquired MR data based on the identified motion (Paragraph 0133, the system can stop the scan, as shown in the flow chart 3100 of Fig. 7, at step 3110, wherein a reason for stopping the scan is that the system has made a determination that a suitable number of useable frames were obtained; this reads on determining a set of useable MR data; furthermore, in the previous step, 3108, a total movement in the patient is determined, and the movement can be a factor in the determination of when to stop the acquisition, Paragraph 0133, such that the scan can be stopped if unacceptably high magnitude movements or a high number of low magnitude motion is detected/determined). Dosenbach also teaches displaying the MRI reports on a display (Paragraph 0165).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Riva-Posse, wherein the computer system analyzes the received MR data to monitor and identify motion in real-time, and determine a set of useable MR data from the acquired MR data based on the identified motion, as taught by Dosenbach, in order to be able to compensate for motion, such as head motion, by determining the quality of the data set in regards to motion in real-time, and overcome the need to overscan (i.e. scanning performing additional scans to act as a buffer to compensate for motion) (Paragraph 0008), such that the operator can continue their scans until they have acquired all the data that they need, and thus would save MRI operational duration and cost (Paragraph 0006).
However, the modifications of Riva-Posse and Dosenbach do not explicitly disclose generating a report indicating the target location.
Moreno teaches generating a report indicating the target location (Paragraph 0244, outputting a report for the patient and/or practitioner, wherein the report may include a visual display of the brain map; Paragraph 0201, wherein the brain map cand include the target treatment locations).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Riva-Posse and Dosenbach, wherein the method includes generating a report indicating the target location, as taught by Moreno, in order to provide a visual representation of the target to the practitioner administering the treatment (Paragraph 0244).
Regarding claim 2, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 1 above.
Riva-Posse teaches wherein the multiple fiber bundles passing through the SCC region comprises cingulum bundle (CM), forceps minor (FM), frontal striatal fibers (F-ST), and uncinate fasciculus (UF) (See Fig. 2 in both a & b, labels for cingulum bundle, CB, forceps minor, FM, frontal striatal fibers, F-St, and uncinate fasciculus, UF, showing the respective bundles passing through the SCC25 region, that reads on the SCC region).
Regarding claim 3, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 1 above.
As disclosed in the claim 1 rejection above, the combination of Riva-Posse, Dosenbach, and Moreno disclose displaying the report on a display. Moreno teaches wherein the report includes a visual display of the brain map (Moreno, Paragraph 0244), and Dosenbach teaches displaying the MRI reports on a display (Dosenbach,, Paragraph 0165). The combination would read on the report with the target location being displayed on the display.
Regarding claims 4 and 5, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 1 above.
Riva-Posse discloses wherein the received MR data is diffusion MR data (Page 2, “Magnetic resonance image acquisition and preprocessing”, diffusion weighted MRI images were collected from the Siemens 3T scanner, which reads on diffusion MR data, acquired using diffusion weighted imaging).
Regarding claim 9, Riva-Posse discloses a system for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI), the system comprising:
a computing device including a processor (See supplemental material section, Page S1, image processing of the image data were performed using software such as FSL; Page S2, DBS modeling for generation of probabilistic tractography maps were generated using deep brain stimulation neurosurgical research software; the method disclosed by Riva-Posses uses various software and thus would read on a computer-implemented method) for brain mapping (probabilistic tract map, Abstract) programmed to:
receive MR data (Page S1, all images were preprocessed using tools from FSL software, wherein the images are diffusion data with T1 images, which would read on MRI data; therefore it is inferred and also common knowledge that the imaging processing software, such as FSL, would run on a computing device such as a general purpose computer, wherein the computer would have at least one processor in communication with at least one memory system, and is in communication to receive data acquired using an MRI system) acquired using an MRI system (High-resolution T1 and diffusion-weighted images were collected within a single session for each subject on a research-dedicated Siemens 3T Tim-Trio scanner, Page 2, right column, “Magnetic resonance image acquisition and pre-processing.”);
generate a map of the subject's brain based on the set of MR data (Page 2, right column, “prospective patient-specific target selection”, generation of the blueprint on each patient’s own MRI-DTI scan, wherein Fig. 2a show’s an exemplary blueprint, with the four fiber bundles overlayed on the brain MRI image); and
identify a target location in the subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain (Fig. 2b, individualized deterministic tractography target selection in one subject, which shows the optimal target location within SCC region with modeled stimulation impacting necessary fiber bundles for effective SCC DBS; wherein Fig. 2b is generated based by determining the target location that best visually matched the blueprint as shown in Fig. 2a), wherein the target location is a point of convergence of multiple fiber bundles passing through the SCC region (as can be seen in Fig. 2b, multiple fiber bundles CB, FM, F-st, and UF converges around the blue sphere, through the SCC region).
However, Riva-Posse does not explicitly disclose analyzing the received MR data to monitor and identify motion in real-time and determining a set of useable MR data from the acquired MR data based on the identified motion.
Dosenbach teaches analyzing, by the computer system, the received MR data to monitor and identify motion in real-time (Title, “Real time monitoring and prediction of motion in mri”; Paragraph 0110, method, system and device for real-time monitoring of motion of a body part of a patient during MRI scanning, wherein the head is provided as an example of the body part; Paragraph 0160, wherein the method is performed using a computing system including one or more computers) and determining, by the computing system, a set of useable MR data from the acquired MR data based on the identified motion (Paragraph 0133, the system can stop the scan, as shown in the flow chart 3100 of Fig. 7, at step 3110, wherein a reason for stopping the scan is that the system has made a determination that a suitable number of useable frames were obtained; this reads on determining a set of useable MR data; furthermore, in the previous step, 3108, a total movement in the patient is determined, and the movement can be a factor in the determination of when to stop the acquisition, Paragraph 0133, such that the scan can be stopped if unacceptably high magnitude movements or a high number of low magnitude motion is detected/determined). Dosenbach also teaches displaying the MRI reports on a display (Dosenbach,, Paragraph 0165),
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Riva-Posse, wherein the computer system analyzes the received MR data to monitor and identify motion in real-time, and determine a set of useable MR data from the acquired MR data based on the identified motion, as taught by Dosenbach, in order to be able to compensate for motion, such as head motion, by determining the quality of the data set in regards to motion in real-time, and overcome the need to overscan (i.e. scanning performing additional scans to act as a buffer to compensate for motion) (Paragraph 0008), such that the operator can continue their scans until they have acquired all the data that they need, and thus would save MRI operational duration and cost (Paragraph 0006).
However, the modifications of Riva-Posse and Dosenbach do not explicitly disclose generating a report indicating the target location.
Moreno teaches generating a report indicating the target location (Paragraph 0244, outputting a report for the patient and/or practitioner, wherein the report may include a visual display of the brain map; Paragraph 0201, wherein the brain map cand include the target treatment locations).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Riva-Posse and Dosenbach, wherein the method includes generating a report indicating the target location, as taught by Moreno, in order to provide a visual representation of the target to the practitioner administering the treatment (Paragraph 0244).
Therefore, the combination of Riva-Posse, Dosenbach, and Moreno would teach displaying the report with the target location, on a display, since Moreno teaches wherein the report includes a visual display of the brain map (Moreno, Paragraph 0244), and Dosenbach teaches displaying the MRI reports on a display (Dosenbach, Paragraph 0165).
Regarding claim 10, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 9 above.
Riva-Posse teaches wherein the multiple fiber bundles passing through the SCC region comprises cingulum bundle (CM), forceps minor (FM), frontal striatal fibers (F-ST), and uncinate fasciculus (UF) (See Fig. 2 in both a & b, labels for cingulum bundle, CB, forceps minor, FM, frontal striatal fibers, F-St, and uncinate fasciculus, UF, showing the respective bundles passing through the SCC25 region, that reads on the SCC region).
Regarding claims 11 and 12, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 9 above.
Riva-Posse discloses wherein the received MR data is diffusion MR data (Page 2, “Magnetic resonance image acquisition and preprocessing”, diffusion weighted MRI images were collected from the Siemens 3T scanner, which reads on diffusion MR data, acquired using diffusion weighted imaging).
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riva-Posse, in view of Dosenbach, and further in view of Moreno, as applied to claim 4 above, and further in view of US2015/0316635 to Stehning et al. “Stehning”.
Regarding claims 6, 7, and 8, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 4 above.
However, the modifications of Riva-Posse, Dosenbach, and Moreno do not disclose wherein the received MR data is acquired for a first number of diffusion directions, determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data, receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system.
Stehning teaches wherein the MR data is acquired for a first number of diffusion directions (Paragraph 0034, Fig. 3, step 48, acquiring DWI data, wherein the DWI data is acquired with gradient field applied in three orthogonal directions, as determined in step 46 of Fig. 3, Paragraph 0033), determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data (Paragraph 0035, Fig. 3, step 50, bulk motion is detected in the acquired DWI data based on comparison of redundant data in the acquired DWI data with different direction gradient axes) , receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system (Paragraph 0037, Fig. 3, Step 54, receiving of the motion corrected DWI image; wherein the motion corrected images are where the motion corrupted DWI data is corrected with acquired DWI data from different gradient directions, different-b-values, and/or resampling locations of the motion corrupted data, which would read on acquiring DWI data with different diffusion directions, different from the first set of DWI data).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Riva-Posse, Dosenbach, and Moreno, wherein the received MR data is acquired for a first number of diffusion directions, determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data, receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system, as taught by Stehning, in order to perform bulk motion avoidance, detection, and correction, when acquiring the DWI MRI data (Stehning, Paragraph 0033).
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riva-Posse, in view of Dosenbach, and further in view of Moreno, as applied to claim 11 above, and further in view of US2015/0316635 to Stehning et al. “Stehning”.
Regarding claims 13, 14, and 15, the modifications of Riva-Posse, Dosenbach, and Moreno disclose all the features of claim 1 above.
However, the modifications of Riva-Posse, Dosenbach, and Moreno do not disclose wherein the received MR data is acquired for a first number of diffusion directions, determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data, receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system.
Stehning teaches wherein the MR data is acquired for a first number of diffusion directions (Paragraph 0034, Fig. 3, step 48, acquiring DWI data, wherein the DWI data is acquired with gradient field applied in three orthogonal directions, as determined in step 46 of Fig. 3, Paragraph 0033), determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data (Paragraph 0035, Fig. 3, step 50, bulk motion is detected in the acquired DWI data based on comparison of redundant data in the acquired DWI data with different direction gradient axes) , receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system (Paragraph 0037, Fig. 3, Step 54, receiving of the motion corrected DWI image; wherein the motion corrected images are where the motion corrupted DWI data is corrected with acquired DWI data from different gradient directions, different-b-values, and/or resampling locations of the motion corrupted data, which would read on acquiring DWI data with different diffusion directions, different from the first set of DWI data).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Riva-Posse, Dosenbach, and Moreno, wherein the received MR data is acquired for a first number of diffusion directions, determining additional different directions different from the first number of diffusion directions based on the identified motion and set of useable MR data, receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system, as taught by Stehning, in order to perform bulk motion avoidance, detection, and correction, when acquiring the DWI MRI data (Stehning, Paragraph 0033).
Conclusion
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/MT/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798